Facilitating custom content extraction rule configurationfor remote capture agents

ABSTRACT

The disclosed embodiments provide a system for extracting custom content from network packets. During operation, the system receives a stream of packets. The system then parses packets in the stream to determine a protocol for each packet. Next, the system applies a custom-content-extraction rule to each packet associated with a target protocol to obtain the extracted content. Then, the system stores the extracted content in events in a data store to facilitate subsequent queries involving the extracted content.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit under 35 U.S.C. § 120 as a continuationof U.S. application Ser. No. 14/609,292, filed Jan. 29, 2015, the entirecontents of which is hereby incorporated by reference as if fully setforth herein. The applicant(s) hereby rescind any disclaimer of claimscope in the parent application(s) or the prosecution history thereofand advise the USPTO that the claims in this application may be broaderthan any claim in the parent application(s).

BACKGROUND Field

The disclosed embodiments relate to techniques for processing networkdata. More specifically, the disclosed embodiments relate to techniquesfor extracting custom content from network packets.

Related Art

Over the past decade, virtualization has triggered a sea change in thefield of network data capture. Almost every network capture productavailable today is a physical hardware appliance that customers have topurchase and configure. In addition, most network data capturetechnologies are built from scratch to serve a specific purpose andaddress the needs of a particular market. For example, network capturesystems may be customized to extract data for security andintrusion-detection purposes, collect network performance data,facilitate Quality of Service (QoS) policies, redirect data, blocknetwork traffic or perform other analysis or management of networktraffic. However, such targeted and fixed implementations of networkcapture technologies are not designed to be easily modifiable to addressdifferent business needs.

One challenge in generalizing a network capture system is to handlelarge volumes of network data. A network capture system typicallymonitors a large number packet streams at various locations in anetwork. Moreover, each packet stream can potentially run at gigabitclock rates, and as a consequence can generate tremendous volumes ofdata. It is theoretically possible to store all of this network data forsubsequent analysis. However, as a practical matter it is infeasible andprohibitively expensive to store such large volumes of network data.Also, much of the data contained in packets is uninteresting; onlyspecific packet fields contain interesting data for a particularpurpose.

Hence, what is needed is a system for capturing network data thatfacilitates selectively retrieving specific data from packets.

SUMMARY

The disclosed embodiments provide a system for extracting custom contentfrom network packets. During operation, the system receives a stream ofpackets. The system then parses packets in the stream to determine aprotocol for each packet. Next, the system applies acustom-content-extraction rule to each packet associated with a targetprotocol to obtain the extracted content. Then, the system stores theextracted content in events in a data store to facilitate subsequentqueries involving the extracted content.

In some embodiments, the system receives a query to be applied to theevents in the data store. In response to this query, the systemretrieves events from the data store, and uses a late-binding schemagenerated from the query to retrieve data values from the events. Thesystem then processes the query using the retrieved data values.

In some embodiments, a rule for determining the target protocol isdifferent than the custom-content-extraction rule that is used to obtainthe extracted content from each packet that matches the target protocol.

In some embodiments, applying the custom-content-extraction rule to agiven packet includes applying a field-specific regular expression toone or more target fields in the packet.

In some embodiments, applying the custom-content-extraction rule to agiven packet includes applying a field-specific extraction rule for astructured data format, which can include XML or JSON, to one or moretarget fields in the packet.

In some embodiments, parsing packets in the stream to determine aprotocol for each packet includes using a deep-packet inspection engineto determine the protocol for each packet.

In some embodiments, the system is additionally configured to obtain thecustom-content-extraction rule from a user through a user interface.

In some embodiments, the user interface comprises a dialog box includingfields for receiving input from a user, wherein the input includes oneor more of: (1) a source field identifier that identifies a source fieldin each packet from which to obtain the extracted content; (2) anextraction rule type that specifies a type of extraction rule to be usedto obtain the extracted content; (3) an extraction rule entered by theuser; and (4) an identifier that is used to identify the extractedcontent.

In some embodiments, the custom context extraction is performed by aremote capture agent located on a network interface on a network. Inthese embodiments, storing the extracted content in the data storeinvolves streaming the events from the remote capture agent to the datastore.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a block diagram of an exemplary event-processing system inaccordance with the disclosed embodiments.

FIG. 2 presents a flowchart illustrating how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments.

FIG. 3 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with the disclosed embodiments.

FIG. 4 presents a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withthe disclosed embodiments.

FIG. 5 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with the disclosed embodiments.

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments.

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments.

FIG. 7A illustrates a key indicators view in accordance with thedisclosed embodiments.

FIG. 7B illustrates an incident review dashboard in accordance with thedisclosed embodiments.

FIG. 7C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments.

FIG. 7D illustrates a screen displaying both log data and performancedata in accordance with the disclosed embodiments.

FIG. 8 shows a schematic of a system in accordance with the disclosedembodiments.

FIG. 9A shows a remote capture agent in accordance with the disclosedembodiments.

FIG. 9B shows the protocol-based capture of network data using a remotecapture agent in accordance with the disclosed embodiments.

FIG. 10 shows a configuration server in accordance with the disclosedembodiments.

FIG. 11A shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 11B shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 11C shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 11D shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 11E shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 11F shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 12A shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 12B shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 13 shows a flowchart illustrating the processing of network data inaccordance with the disclosed embodiments.

FIG. 14 shows a flowchart illustrating the process of usingconfiguration information associated with a protocol classification tobuild an event stream from a packet flow in accordance with thedisclosed embodiments.

FIG. 15 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.

FIG. 16 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.

FIG. 17A shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 17B shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 17C shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 17D shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 17E shows an exemplary screenshot in accordance with the disclosedembodiments.

FIG. 18 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.

FIG. 19 shows a flowchart illustrating the process of displaying eventstream information represented by a grouping of the event streams by anevent stream attribute in accordance with the disclosed embodiments.

FIG. 20 presents a flowchart illustrating the process of facilitatingthe processing of network data in accordance with the disclosedembodiments.

FIG. 21 presents a flowchart illustrating the process of facilitatingthe processing of network data in accordance with the disclosedembodiments.

FIG. 22 presents a flowchart illustrating the process of facilitatingthe processing of network data in accordance with the disclosedembodiments.

FIG. 23A presents a block diagram illustrating a system that facilitatescustom content extraction in accordance with the disclosed embodiments.

FIG. 23B illustrates how extracted content obtained from a packet isincorporated into an event in accordance with the disclosed embodiments.

FIG. 24 presents a flowchart illustrating how the packets are processedto facilitate custom content extraction in accordance with the disclosedembodiments.

FIG. 25 presents a flowchart illustrating how a query is processed inaccordance with the disclosed embodiments.

FIG. 26 illustrates a dialog box that is configured to receive acustom-content-extraction rule in accordance with the disclosedembodiments.

FIG. 27 shows a computer system in accordance with the disclosedembodiments.

In the figures, like reference numerals refer to the same figureelements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the embodiments, and is provided in the contextof a particular application and its requirements. Various modificationsto the disclosed embodiments will be readily apparent to those skilledin the art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present disclosure. Thus, the present invention is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. The computer-readable storage medium includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disk drives, magnetic tape, CDs (compact discs),DVDs (digital versatile discs or digital video discs), or other mediacapable of storing code and/or data now known or later developed.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage medium as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage medium, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium.

Furthermore, methods and processes described herein can be included inhardware modules or apparatus. These modules or apparatus may include,but are not limited to, an application-specific integrated circuit(ASIC) chip, a field-programmable gate array (FPGA), a dedicated orshared processor that executes a particular software module or a pieceof code at a particular time, and/or other programmable-logic devicesnow known or later developed. When the hardware modules or apparatus areactivated, they perform the methods and processes included within them.

1.1 Overview

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that was selected at ingestion time.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” wherein each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time-series data,” wherein time-series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, whereinspecific data items with specific data formats reside at predefinedlocations in the data. For example, structured data can include dataitems stored in fields in a database table. In contrast, unstructureddata does not have a predefined format. This means that unstructureddata can comprise various data items having different data types thatcan reside at different locations. For example, when the data source isan operating system log, an event can include one or more lines from theoperating system log containing raw data that includes different typesof performance and diagnostic information associated with a specificpoint in time. Examples of data sources from which an event may bederived include, but are not limited to: web servers; applicationservers; databases; firewalls; routers; operating systems; and softwareapplications that execute on computer systems, mobile devices, andsensors. The data generated by such data sources can be produced invarious forms including, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements and sensor measurements. An eventtypically includes a timestamp that may be derived from the raw data inthe event, or may be determined through interpolation between temporallyproximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, wherein theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly,” when it isneeded (e.g., at search time), rather than at ingestion time of the dataas in traditional database systems. Because the schema is not applied toevent data until it is needed (e.g., at search time), it is referred toas a “late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction rulecomprises a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timeat which a query is actually executed. This means that extraction rulesfor the fields in a query may be provided in the query itself, or may belocated during execution of the query. Hence, as an analyst learns moreabout the data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about theevents rather than data in the events themselves can be createdautomatically. For example, such default fields can specify: a timestampfor the event data; a host from which the event data originated; asource of the event data; and a source type for the event data. Thesedefault fields may be determined automatically when the events arecreated, indexed or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

1.2 Data Server System

FIG. 1 shows a block diagram of an exemplary event-processing system100, similar to the SPLUNK® ENTERPRISE system. System 100 includes oneor more forwarders 101 that collect data obtained from a variety ofdifferent data sources 105, and one or more indexers 102 that store,process, and/or perform operations on this data, wherein each indexeroperates on data contained in a specific data store 103. Theseforwarders and indexers can comprise separate computer systems in a datacenter, or may alternatively comprise separate processes executing onvarious computer systems in a data center.

During operation, forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers 102. Forwarders 101 can also perform operations to stripextraneous data and detect timestamps in the data. Forwarders 101 maynext determine which indexers 102 will receive each data item andforward the data items to the determined indexers 102. Indexers 102 maythen provide the data for storage in one or more data stores 103.

As mentioned above, the data may include streams, logs, databaserecords, messages, archives, and/or other records containing time-seriesdata. Time-series data refers to any data that can be associated with atimestamp. The data can be structured, unstructured, or semi-structuredand come from files or directories. Unstructured data may include data,such as machine data and web logs, that is not organized to facilitateextraction of values for fields from the data.

Note that distributing data across different indexers facilitatesparallel processing. This parallel processing can take place at dataingestion time, because multiple indexers can process the incoming datain parallel. The parallel processing can also take place at search time,because multiple indexers can search the data in parallel.

System 100 and the processes described below with respect to FIGS. 1-5are further described in “Exploring Splunk Search Processing Language(SPL) Primer and Cookbook,” by David Carasso, CITO Research, 2012, andin “Optimizing Data Analysis With a Semi-Structured Time-seriesDatabase,” by Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, andSteve Zhang, SLAML, 2010, each of which is hereby incorporated herein byreference in its entirety for all purposes.

1.3 Data Ingestion

FIG. 2 presents a flowchart illustrating how an indexer processes,indexes, and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, wherein the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, wherein the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2.”

Finally, the indexer stores the events in a data store at block 208,wherein a timestamp can be stored with each event to facilitatesearching for events based on a time range. In some cases, the storedevents are organized into a plurality of buckets, wherein each bucketstores events associated with a specific time range. This not onlyimproves time-based searches, but it also allows events with recenttimestamps that may have a higher likelihood of being accessed to bestored in faster memory to facilitate faster retrieval. For example, abucket containing the most recent events can be stored as flash memoryinstead of on hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,wherein each indexer returns partial responses for a subset of events toa search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as is described in U.S. patent application Ser. No. 14/266,812filed on 30 Apr. 2014, and in U.S. patent application Ser. No.14/266,817 also filed on 30 Apr. 2014.

1.4 Query Processing

FIG. 3 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient at block 301. Next, at block 302, the search head analyzes thesearch query to determine what portions can be delegated to indexers andwhat portions need to be executed locally by the search head. At block303, the search head distributes the determined portions of the query tothe indexers. Note that commands that operate on single events can betrivially delegated to the indexers, while commands that involve eventsfrom multiple indexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding schema to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending upon what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

1.5 Field Extraction

FIG. 4 presents a block diagram illustrating how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 402 is received at a queryprocessor 404. Query processor 404 includes various mechanisms forprocessing a query, wherein these mechanisms can reside in a search head104 and/or an indexer 102. Note that the exemplary search query 402illustrated in FIG. 4 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. SPL isa pipelined search language in which a set of inputs is operated on by afirst command in a command line, and then a subsequent command followingthe pipe symbol “1” operates on the results produced by the firstcommand, and so on for additional commands. Search query 402 can also beexpressed in other query languages, such as the Structured QueryLanguage (“SQL”) or any suitable query language.

Upon receiving search query 402, query processor 404 sees that searchquery 402 includes two fields “IP” and “target.” Query processor 404also determines that the values for the “IP” and “target” fields havenot already been extracted from events in data store 414, andconsequently determines that query processor 404 needs to use extractionrules to extract values for the fields. Hence, query processor 404performs a lookup for the extraction rules in a rule base 406, whereinrule base 406 maps field names to corresponding extraction rules andobtains extraction rules 408-409, wherein extraction rule 408 specifieshow to extract a value for the “IP” field from an event, and extractionrule 409 specifies how to extract a value for the “target” field from anevent. As is illustrated in FIG. 4, extraction rules 408-409 cancomprise regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, query processor 404 sends extraction rules 408-409 to a fieldextractor 412, which applies extraction rules 408-409 to events 416-418in a data store 414. Note that data store 414 can include one or moredata stores, and extraction rules 408-409 can be applied to largenumbers of events in data store 414, and are not meant to be limited tothe three events 416-418 illustrated in FIG. 4. Moreover, the queryprocessor 404 can instruct field extractor 412 to apply the extractionrules to all the events in a data store 414, or to a subset of theevents that have been filtered based on some criteria.

Next, field extractor 412 applies extraction rule 408 for the firstcommand “Search IP=“10*” to events in data store 414 including events416-418. Extraction rule 408 is used to extract values for the IPaddress field from events in data store 414 by looking for a pattern ofone or more digits, followed by a period, followed again by one or moredigits, followed by another period, followed again by one or moredigits, followed by another period, and followed again by one or moredigits. Next, field extractor 412 returns field values 420 to queryprocessor 404, which uses the criterion IP=“10*” to look for IPaddresses that start with “10”. Note that events 416 and 417 match thiscriterion, but event 418 does not, so the result set for the firstcommand is events 416-417.

Query processor 404 then sends events 416-417 to the next command “statscount target.” To process this command, query processor 404 causes fieldextractor 412 to apply extraction rule 409 to events 416-417. Extractionrule 409 is used to extract values for the target field for events416-417 by skipping the first four commas in events 416-417, and thenextracting all of the following characters until a comma or period isreached. Next, field extractor 412 returns field values 421 to queryprocessor 404, which executes the command “stats count target” to countthe number of unique values contained in the target fields, which inthis example produces the value “2” that is returned as a final result422 for the query.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

1.6 Exemplary Search Screen

FIG. 6A illustrates an exemplary search screen 600 in accordance withthe disclosed embodiments. Search screen 600 includes a search bar 602that accepts user input in the form of a search string. It also includesa time range picker 612 that enables the user to specify a time rangefor the search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, for example by selecting specifichosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, wherein search results tabs 604includes: an “events tab” that displays various information about eventsreturned by the search; a “statistics tab” that displays statisticsabout the search results; and a “visualization tab” that displaysvarious visualizations of the search results. The events tab illustratedin FIG. 6A displays a timeline 605 that graphically illustrates thenumber of events that occurred in one-hour intervals over the selectedtime range. It also displays an events list 608 that enables a user toview the raw data in each of the returned events. It additionallydisplays a fields sidebar 606 that includes statistics about occurrencesof specific fields in the returned events, including “selected fields”that are pre-selected by the user, and “interesting fields” that areautomatically selected by the system based on pre-specified criteria.

1.7 Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

1.7.1 Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 5 illustrates how a searchquery 501 received from a client at search head 104 can split into twophases, including: (1) a “map phase” comprising subtasks 502 (e.g., dataretrieval or simple filtering) that may be performed in parallel and are“mapped” to indexers 102 for execution, and (2) a “reduce phase”comprising a merging operation 503 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 3, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

1.7.2 Keyword Index

As described above with reference to the flow charts in FIGS. 2 and 3,event-processing system 100 can construct and maintain one or morekeyword indices to facilitate rapidly identifying events containingspecific keywords. This can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

1.7.3 High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100make use of a high performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an exemplary entry in a summarization table can keep track ofoccurrences of the value “94107” in a “ZIP code” field of a set ofevents, wherein the entry includes references to all of the events thatcontain the value “94107” in the ZIP code field. This enables the systemto quickly process queries that seek to determine how many events have aparticular value for a particular field, because the system can examinethe entry in the summarization table to count instances of the specificvalue in the field without having to go through the individual events ordo extractions at search time. Also, if the system needs to process allevents that have a specific field-value combination, the system can usethe references in the summarization table entry to directly access theevents to extract further information without having to search all ofthe events to find the specific field-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, wherein a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, wherein theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover all of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search otherevents that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

1.7.4 Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. (This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods.) If reportscan be accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only events within thetime period that meet the specified criteria. Similarly, if the queryseeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so only the newer eventdata needs to be processed while generating an updated report. Thesereport acceleration techniques are described in more detail in U.S. Pat.No. 8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696,issued on Apr. 2, 2011.

1.8 Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that make it easy for developers to create applicationsto provide additional capabilities. One such application is the SPLUNK®APP FOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, wherein the extracteddata is typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. (The process of detecting securitythreats for network-related information is further described in U.S.patent application Ser. Nos. 13/956,252, and 13/956,262.)Security-related information can also include endpoint information, suchas malware infection data and system configuration information, as wellas access control information, such as login/logout information andaccess failure notifications. The security-related information canoriginate from various sources within a data center, such as hosts,virtual machines, storage devices and sensors. The security-relatedinformation can also originate from various sources in a network, suchas routers, switches, email servers, proxy servers, gateways, firewallsand intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 7A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 700 additionallydisplays a histogram panel 704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 7B illustrates an exemplary incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further in“http://docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

1.9 Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc., of Palo Alto, Calif. For example, these performancemetrics can include: (1) CPU-related performance metrics; (2)disk-related performance metrics; (3) memory-related performancemetrics; (4) network-related performance metrics; (5) energy-usagestatistics; (6) data-traffic-related performance metrics; (7) overallsystem availability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00,http://pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Exemplary node-expansion operations are illustrated in FIG.7C, wherein nodes 733 and 734 are selectively expanded. Note that nodes731-739 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/235,490 filed on 15 Apr. 2014, which is hereby incorporated herein byreference for all possible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data and associated performance metrics, for theselected time range. For example, the screen illustrated in FIG. 7Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 742 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316 filed on 29Jan. 2014, which is hereby incorporated herein by reference for allpossible purposes.

2.1 Managing Event Streams Generated from Captured Network Data

The disclosed embodiments provide a method and system for facilitatingthe processing of network data. As shown in FIG. 8, the network data maybe captured using a data-processing system 800 in a distributed networkenvironment. In the illustrated embodiment, system 800 includes a set ofconfiguration servers 820 in communication with a set of remote captureagents 851-853 over one or more networks 890.

Although system 800 only depicts three configuration servers 820 andthree remote capture agents 851-853, any number of configuration servers820 and/or remote capture agents 851-853 may be configured to operateand/or communicate with one another within the data-processing system.For example, a single physical and/or virtual server may perform thefunctions of configuration servers 820. Alternatively, multiple physicaland/or virtual servers or network elements may be logically connected toprovide the functionality of configuration servers 820. Theconfiguration server(s) may direct the activity of multiple distributedremote capture agents 851-853 installed on various client computingdevices across one or more networks. In turn, remote capture agents851-853 may be used to capture network data from multiple remote networkdata sources.

Further, embodiments described herein can be configured to capturenetwork data in a cloud-based environment, such as cloud 840 depicted inthe illustrated embodiment, and to generate events such as timestampedrecords of network activity from the network data. Remote capture agents851-853 may capture network data originating from numerous distributednetwork servers, whether they are physical hardware servers or virtualmachines running in cloud 840. In cloud-based implementations, remotecapture agents 851-853 will generally only have access to informationthat is communicated to and received from machines running in thecloud-based environment. This is because, in a cloud environment, thereis generally no access to any of the physical network infrastructure, ascloud computing may utilize a “hosted services” delivery model where thephysical network infrastructure is typically managed by a third party.

Embodiments further include the capability to separate the data capturetechnology into a standalone component that can be installed directly onclient servers, which may be physical servers or virtual machinesresiding on a cloud-based network (e.g., cloud 840), and used to captureand generate events for all network traffic that is transmitted in andout of the client servers. This eliminates the need to deploy andconnect physical hardware to network TAPS or SPAN ports, thus allowingusers to configure and change their data capture configurationon-the-fly rather than in fixed formats.

In the illustrated embodiment, remote capture agents 852-853 are incommunication with network servers 830 residing in cloud 840, and remotecapture agent 851 is located in cloud 840. Cloud 840 may represent anynumber of public and private clouds, and is not limited to anyparticular cloud configuration. Network servers 830 residing in cloud840 may be physical servers and/or virtual machines in cloud 840, andnetwork traffic to and from network servers 830 may be monitored byremote capture agent 851 and/or other remote capture agents connected tonetwork servers 830. Further, remote capture agents 852-853 may also runin cloud 840 on physical servers and/or virtual machines. Those skilledin the art will appreciate that any number of remote capture agents maybe included inside or outside of cloud 840.

Remote capture agents 851-853 may analyze network packets received fromthe networks(s) to which remote capture agents 851-853 are connected toobtain network data from the network packets and generate a number ofevents from the network data. For example, each remote capture agent851-853 may listen for network traffic on network interfaces availableto the remote capture agent. Network packets transmitted to and/or fromthe network interfaces may be intercepted by the remote capture agentand analyzed, and relevant network data from the network packets may beused by the remote capture agent to create events related to the networkdata. Such events may be generated by aggregating network data frommultiple network packets, or each event may be generated using thecontents of only one network packet. A sequence of events from a remotecapture agent may then be included in one or more event streams that areprovided to other components of system 800.

Configuration servers 820, data storage servers 835, and/or othernetwork components may receive event data (e.g., event streams) fromremote capture agents 851-853 and further process the event data beforethe event data is stored by data storage servers 835. In the illustratedembodiment, configuration servers 820 may transmit event data to datastorage servers 835 over a network 801 such as a local area network(LAN), wide area network (WAN), personal area network (PAN), virtualprivate network, intranet, mobile phone network (e.g., a cellularnetwork), Wi-Fi network, Ethernet network, and/or other type of networkthat enables communication among computing devices. The event data maybe received over a network (e.g., network 801, network 890) at one ormore event indexers (see FIG. 1) associated with data storage servers835.

In addition, system 800 may include functionality to determine the typesof network data collected and/or processed by each remote capture agent851-853 to avoid data duplication at the indexers, data storage servers835, and/or other components of system 800. For example, remote captureagents 852-853 may process network traffic from the same network.However, remote capture agent 852 may generate page view events from thenetwork traffic, and remote capture agent 853 may generate requestevents (e.g., of HyperText Transfer Protocol (HTTP) requests andresponses) from the network traffic.

In one or more embodiments, configuration servers 820 includeconfiguration information that is used to configure the creation ofevents from network data on remote capture agents 851-853. In addition,such configuration may occur dynamically during event processing (e.g.,at runtime). Conversely, because most conventional network capturetechnologies target specific end uses, they have been designed tooperate in a fixed way and generally cannot be modified dynamically oreasily to address different and changing business needs.

At least certain embodiments are adapted to provide a distributed remotecapture platform in which the times at which events are communicated tothe configuration servers 820 and the fields to be included in theevents are controlled by way of user-modifiable configuration ratherthan by “hard coding” fixed events with pre-determined fields for agiven network capture mechanism. The remote configuration capabilityalso enables additional in-memory processing (e.g., filtering,transformation, normalization, aggregation, etc.) on events at the pointof capture (e.g., remote capture agents 851-853) before the events aretransmitted to other components of system 800.

Configuration information stored at each configuration server 820 may becreated and/or updated manually at the configuration server and/or at anetwork element in communication with the configuration server. Forexample, a user may upload a configuration file containing configurationinformation for a remote capture agent to one or more configurationservers 820 for subsequent propagation to the remote capture agent.Alternatively, the user may use a GUI to provide the configurationinformation, as described in further detail below with respect to FIGS.11A-11D. The configuration information may further be provided by one ormore applications miming on a separate server or network element, suchas data storage servers 835.

Remote capture agents 851-853 may then use the configuration informationto generate events from captured network packets. When changes in theconfiguration information at the configuration server are detected atthe remote capture agents, logic in the remote capture agents may beautomatically reconfigured in response. This means the remote captureagents may be dynamically configured to produce different events,transform the events, and/or communicate event streams to differentcomponents of system 800. Dynamic configuration of the generation ofevents from captured network packets may also be performed by othercomponents (e.g., configuration servers 820, data storage servers 835,etc.), in lieu of or in addition to the remote capture agents.

To detect changes in configuration information at configuration servers820, remote capture agents 851-853 may poll configuration servers 820 atperiodic intervals for updates to the configuration information. Theupdates may then be pulled from configuration servers 820 by remotecapture agents 851-853. Conversely, updates to the configurationinformation may be pushed from configuration servers 820 to remotecapture agents 851-853 at periodic intervals and/or when changes to theconfiguration information have been made.

In one embodiment, configuration servers 820 include a list of eventstreams generated by remote capture agents 851-853, as well as theconfiguration information used to generate the event streams at remotecapture agents 851-853. The configuration information may include aunique identifier for each event stream, the types of events to beincluded in the event stream, one or more fields to be included in eachevent, and/or one or more filtering rules for filtering events to beincluded in the event stream. Using configuration information todynamically modify network data capture by remote capture agents (e.g.,remote capture agents 851-853) is described in a co-pendingnon-provisional application by inventor Michael Dickey, entitled“Distributed Processing of Network Data Using Remote Capture Agents,”having Ser. No. 14/253,783, and filing date 15 Apr. 2014 (AttorneyDocket No. SPLK14-1006), which is incorporated herein by reference.

In one or more embodiments, system 800 includes functionality to performprotocol-based capture and analysis of network data using remote captureagents 851-853. First, remote capture agents 851-853 may be configuredto generate event streams from packet flows captured at remote captureagents 851-853 based on protocol classifications for the packet flows.Second, configuration servers 820 may include functionality tostreamline the configuration of remote capture agents 851-853 ingenerating protocol-specific event streams. Third, configuration servers820 and/or remote capture agents 851-853 may enable the use of capturetriggers to capture additional network data based on the identificationof potential security risks from previously generated event streams.Protocol-based capture and analysis of network data using remote captureagents is described in a co-pending non-provisional application byinventors Vladimir Shcherbakov and Michael Dickey and filed on the sameday as the instant application, entitled “Protocol-Based Capture ofNetwork Data Using Remote Capture Agents,” having Ser. No. 14/528,898,and filing date 30 Oct. 2014 (Attorney Docket No. SPLK14-1021), which isincorporated herein by reference.

FIG. 9 shows a remote capture agent 950 in accordance with the disclosedembodiments. In the illustrated embodiment, remote capture agent 950 isadapted to receive configuration information from one or moreconfiguration servers 820 over network 801. Remote capture agent 950 maybe installed at a customer's premises on one or more of the customer'scomputing resources. Remote capture agent 950 may also be installed in aremote computing environment such as a cloud computing system. Forexample, remote capture agent 950 may be installed on a physical serverand/or in a virtual computing environment (e.g., virtual machine) thatis distributed across one or more physical machines.

Remote capture agent 950 includes a communications component 903configured to communicate with network elements on one or more networks(e.g., network 801) and send and receive network data (e.g., networkpackets) over the network(s). As depicted, communications component 903may communicate with configuration servers 820 over network 801.Communications component 903 may also communicate with one or moresources of network data, such as network servers 830 of FIG. 8.

Network data received at communications component 903 may be captured bya capture component 905 coupled with communications component 903.Capture component 905 may capture some or all network data fromcommunications component 903. For example, capture component 905 maycapture network data based on the sources and/or destinations of thenetwork data, the types of the network data, the protocol associatedwith the network data, and/or other characteristics of the network data.

In addition, the network data may be captured based on configurationinformation stored in a configuration component 904 of remote captureagent 950. As mentioned above, the configuration information may bereceived from configuration servers 820 over network 801. Theconfiguration information may then be used to dynamically configure orreconfigure remote capture agent 950 in real-time. For example, newlyreceived configuration information in configuration component 904 may beused to configure the operation of remote capture agent 950 duringprocessing of events from network data by remote capture agent 950.

To dynamically configure remote capture agent 950, configurationinformation received by configuration component 904 from configurationservers 820 may be provided to other components of remote capture agent950. More specifically, remote capture agent 950 includes an eventsgenerator 907 that receives network data from network data capturecomponent 905 and generates events from the network data based onconfiguration information from configuration component 904.

Using configuration information provided by configuration servers 820,remote capture agent 950 can be instructed to perform any number ofevent-based processing operations. For example, the configurationinformation may specify the generation of event streams associated withnetwork (e.g., HTTP, Simple Mail Transfer Protocol (SMTP), Domain NameSystem (DNS)) transactions, business transactions, errors, alerts,clickstream events, and/or other types of events. The configurationinformation may also describe custom fields to be included in theevents, such as values associated with specific clickstream terms. Theconfiguration information may include additional parameters related tothe generation of event data, such as an interval between consecutiveevents and/or the inclusion of transactions and/or errors matching agiven event in event data for the event. Configuration information forconfiguring the generation of event streams from network data capturedby remote capture agents is further described in the above-referencedapplications.

An events transformer 909 may further use the configuration informationto transform some or all of the network data from capture component 905and/or events from events generator 907 into one or more sets oftransformed events. In one or more embodiments, transformationsperformed by events transformer 909 include aggregating, filtering,cleaning, and/or otherwise processing events from events generator 907.Configuration information for the transformations may thus include anumber of parameters that specify the types of transformations to beperformed, the types of data on which the transformations are to beperformed, and/or the formatting of the transformed data.

A rules comparison engine 908 in remote capture agent 950 may receiveevents from events generator 907 and compare one or more fields from theevents to a set of filtering rules in the configuration information todetermine whether to include the events in an event stream. For example,the configuration information may specify packet-level, protocol-level,and/or application-level filtering of event data from event streamsgenerated by remote capture agent 950.

Finally, a data enrichment component 911 may further transform eventdata into a different form or format based on the configurationinformation from configuration component 904. For example, dataenrichment component 911 may use the configuration information tonormalize the data so that multiple representations of the same value(e.g., timestamps, measurements, etc.) are converted into the same valuein transformed event data.

Data can be transformed by data enrichment component 911 in any numberof ways. For example, remote capture agent 950 may reside on a clientserver in Cupertino, Calif., where all the laptops associated with theclient server have been registered with the hostname of the clientserver. Remote capture agent 950 may use the registration data to lookup an Internet Protocol (IP) address in a look-up table (LUT) that isassociated with one or more network elements of the client server'slocal network. Remote capture agent 950 may then resolve a user's IPaddress into the name of the user's laptop, thereby enabling inclusionof the user's laptop name in transformed event data associated with theIP address. The transformed event data may then be communicated toconfiguration servers 820 and/or a central transformation serverresiding in San Francisco for further processing, indexing, and/orstorage.

As mentioned above, remote capture agent 950 may perform protocol-basedgeneration of event streams from network data. As shown in FIG. 9B,configuration component 904 may obtain protocol-specific configurationinformation (e.g., protocol-specific configuration information A 912,protocol-specific configuration information B 914) from one or moreconfiguration servers (e.g., configuration servers 820). For example,configuration information from the configuration server(s) may betransmitted over network 801 to communications component 903, whichprovides the configuration information to configuration component 904for storage and/or further processing.

Protocol-specific configuration information from configuration component904 may be used to configure the generation of event streams (e.g.,event stream C 932, event stream D 934, event stream E 940, event streamF 942) based on protocol classifications of network packets (e.g.,network packets C 916, network packets D 918) captured by capturecomponent 905. For example, protocol-specific configuration informationfrom configuration component 904 may specify the creation of eventstreams from the network packets based on the protocols used in thenetwork packets, such as HTTP, DNS, SMTP, File Transfer Protocol (FTP),Server Message Block (SMB), Network File System (NFS), Internet ControlMessage Protocol (ICMP), email protocols, database protocols, and/orsecurity protocols. Such event streams may include event attributes thatare of interest to the respective protocols.

Before the event streams are generated from the network packets, capturecomponent 905 may assemble the network packets into one or more packetsflows (e.g., packet flow C 920, packet flow D 922). First, capturecomponent 905 may identify the network packets in a given packet flowbased on control information in the network packets. The packet flow mayrepresent a communication path between a source and a destination (e.g.,host, multicast group, broadcast domain, etc.) on the network. As aresult, capture component 905 may identify network packets in the packetflow by examining network (e.g., IP) addresses, ports, sources,destinations, and/or transport protocols (e.g., Transmission ControlProtocol (TCP), User Datagram Protocol (UDP), etc.) from the headers ofthe network packets.

Next, capture component 905 may assemble the packet flow from thenetwork packets. For example, capture component 905 may assemble a TCPpacket flow by rearranging out-of-order TCP packets. Conversely, capturecomponent 905 may omit reordering of the network packets in the packetflow if the network packets use UDP and/or another protocol that doesnot provide for ordered packet transmission.

After the packet flow is assembled, capture component 905 and/or anothercomponent of remote capture agent 950 may detect encryption of thenetwork packets in the packet flow by analyzing the byte signatures ofthe network packets' payloads. For example, the component may analyzethe network packets' payloads for byte signatures that are indicative ofSecure Sockets Layer (SSL) and/or Transport Layer Security (TLS)encryption. If the network packets are detected as encrypted, thecomponent may decrypt the network packets. For example, the componentmay have access to private keys from an SSL server used by the networkflow and perform decryption of the network packets to obtain plaintextpayload data in the order in which the data was sent. Such access toprivate keys may be given to remote capture agent 950 by anadministrator associated with the network flow, such as an administratorof the host from which the network packets are transmitted.

Events generator 907 may then obtain a protocol classification (e.g.,protocol classification C 924, protocol classification D 926) for eachpacket flow identified, assembled, and/or decrypted by capture component905. For example, events generator 907 may use a protocol-decodingmechanism to analyze the headers and/or payloads of the network packetsin the packet flow and return protocol identifiers of one or moreprotocols used in the network packets. The protocol-decoding mechanismmay additionally provide metadata related to the protocols, such asmetadata related to traffic volume, application usage, applicationperformance, user and/or host identifiers, content (e.g., media, files,etc.), and/or file metadata (e.g., video codecs and bit rates).

Once the protocol classification is obtained for a packet flow, eventsgenerator 907 may use protocol-specific configuration informationassociated with the protocol classification from configuration component904 to build an event stream (e.g., event stream C 932, event stream D934) from the packet flow. As mentioned above and in theabove-referenced application, the event stream may include time-seriesevent data generated from network packets in the packet flow. To createthe event stream, events generator 907 may obtain one or more eventattributes associated with the protocol classification from theconfiguration information. Next, event generator 907 may extract theevent attribute(s) from the network packets in the first packet flow.Events generator 907 may then include the extracted event attribute(s)in the event stream.

For example, events generator 907 may obtain a protocol classificationof DNS for a packet flow from capture component 905 andprotocol-specific configuration information for generating event streamsfrom DNS traffic from configuration component 904. The protocol-specificconfiguration information may specify the collection of event attributessuch as the number of bytes transferred between the source anddestination, network addresses and/or identifiers for the source anddestination, DNS message type, DNS query type, return message, responsetime to a DNS request, DNS transaction identifier, and/or a transportlayer protocol. In turn, events generator 907 may parse theprotocol-specific configuration to identify the event attributes to becaptured from the packet flow. Next, events generator 907 may extractthe specified event attributes from the network packets in the packetflow and/or metadata received with the protocol classification of thepacket flow and generate time-stamped event data from the extractedevent attributes. Events generator 907 may then provide the time-stampedevent data in an event stream to communications component 903 fortransmission of the event stream over a network to one or moreconfiguration servers, data storage servers, indexers, and/or othercomponents for subsequent storage and processing of the event stream bythe component(s).

As described above and in the above-referenced application, network datafrom capture component 905 and/or event data from events generator 907may be transformed by events transformer 909 into transformed event datathat is provided in lieu of or in addition to event data generated byevents generator 907. For example, events transformer 909 may aggregate,filter, clean, and/or otherwise process event attributes from eventsgenerator 907 to produce one or more sets of transformed eventattributes (e.g., transformed event attributes 1 936, transformed eventattributes z 938). Events transformer 909 may then include thetransformed event attributes into one or more additional event streams(e.g., event stream 1 940, event stream z 942) that may be transmittedover the network for subsequent storage and processing of the eventstream(s) by other components on the network. Such transformation ofevent data at remote capture agent 950 may offload subsequent processingof the event data at configuration servers and/or other components onthe network. Moreover, if the transformation reduces the size of theevent data (e.g., by aggregating the event data), network trafficbetween remote capture agent 950 and the other components may bereduced, along with the storage requirements associated with storing theevent data at the other components.

As with protocol-based generation of event data by events generator 907,events transformer 909 may use protocol-specific configurationinformation from configuration component 904 to transform network and/orevent data from a given packet flow and/or event stream. For example,events transformer 909 may obtain protocol-specific configurationinformation for aggregating HTTP events and use the configurationinformation to generate aggregated HTTP events from HTTP events producedby events generator 907. The configuration information may include oneor more key attributes used to generate a unique key representing anaggregated event from the configuration information. For example, keyattributes for generating an aggregated HTTP event may include thesource and destination IP addresses and ports in a set of HTTP events. Adifferent unique key and aggregated HTTP event may thus be generated foreach unique combination of source and destination IP addresses and portsin the HTTP events.

The configuration information may also specify one or more aggregationattributes to be aggregated prior to inclusion in the aggregated event.For example, aggregation attributes for generating an aggregated HTTPevent from HTTP event data may include the number of bytes and packetssent in each direction between the source and destination. Datarepresented by the aggregation attributes may be included in theaggregated HTTP event by summing, averaging, and/or calculating asummary statistic from the number of bytes and packets sent in eachdirection between the source and destination. Aggregation of event datais described in further detail below with respect to FIG. 11C.

FIG. 10 shows a configuration server 1020 in accordance with thedisclosed embodiments. As shown in the illustrated embodiment,configuration server 1020 is in communication with multiple remotecapture agents 1050 over network 890, and remote capture agents 1050 aredistributed throughout network 890 and cloud 840. Configuration server1020 includes a communications component 1010 that receives events fromremote capture agents 1050 over network 890 and/or from cloud 840.Communications component 1010 may also communicate with one or more datastorage servers, such as data storage servers 835 of FIG. 8.

Configuration server 1020 also includes a configuration component 1004that stores configuration information for remote capture agents 1050. Asdescribed above, the configuration information may specify the types ofevents to produce, data to be included in the events, and/ortransformations to be applied to the data and/or events to producetransformed events. Some or all of the transformations may be specifiedin a set of filtering rules 1021 that may be applied to event data atremote capture agents 1050 to determine a subset of the event data to beincluded in one or more event streams that are sent to configurationserver 1020 and/or other components.

Configuration information from configuration component 1004 may also beused to manage an event stream lifecycle of the event streams. The eventstream lifecycle may be a permanent event stream lifecycle, in whichgeneration of events in an event stream continues after the eventstream's creation until the event stream is manually disabled, deleted,or otherwise inactivated. Conversely, the event stream lifecycle may bean ephemeral event stream lifecycle, in which events in an event streamare generated on a temporary basis, and the event stream has an end timeat which the event stream is terminated. For example, an ephemeral eventstream may be created by a capture trigger for generating additionaltime-series event data from the network packets on remote capture agents1050 based on a security risk, as described above and in theabove-referenced applications.

To distinguish between permanent and ephemeral event streams, theconfiguration information may include a parameter that identifies eachevent stream as “permanent” or “ephemeral.” The configurationinformation may also include attributes such as a start time and endtime for each ephemeral event stream. Remote capture agents 1050 maybegin generating time-series event data for the ephemeral event streamat the start time and terminate the ephemeral event stream at the endtime.

Alternatively, the creation and termination of an ephemeral event streammay be managed by configuration component 1004 instead of remote captureagents 1050. For example, configuration component 1004 may track thestart and end times of an ephemeral event stream. At the start time ofthe ephemeral event stream, configuration component 1004 may provideremote capture agents 1050 with configuration information for theephemeral event stream. At the end time of the ephemeral end stream,configuration component 1004 may remove all references to the ephemeralevent stream from the configuration information and transmit theconfiguration information to remote capture agents 1050. Becauseconfiguration component 1004 uses updates to the configurationinformation to create and terminate an ephemeral event stream, remotecapture agents 1050 may not be required to distinguish between ephemeralevent streams and permanent event streams.

Configuration server 1020 may also include a data processing component1011 that performs additional processing of the event streams based onconfiguration information from configuration component 1004. Asdiscussed in the above example with respect to FIGS. 9A-9B, event datamay be transformed at a remote capture agent (e.g., remote capture agent950) during resolution of the user's IP address into the name of theuser's laptop. The transformed event data may be sent to configurationserver 1020 and/or a transformation server for additional processingand/or transformation, such as taking the host name from the transformedevent data, using an additional LUT to obtain a user identifier (userID) of the person to which the laptop is registered, and furthertransforming the event data by including the user ID in the event databefore forwarding the event data to a third server (e.g., atransformation server) for another round of processing.

In one or more embodiments, configuration server 1020 and remote captureagents 1050 include functionality to improve the management of eventstreams generated from captured network data, including event streamsassociated with various protocols, applications, and event streamlifecycles. As shown in FIG. 10, configuration server 1020 may provide aGUI 1025 that can be used to configure or reconfigure the informationcontained in configuration component 1004. The configuration informationfrom configuration component 1004 may then be propagated to remotecapture agents 1050 and used by remote capture agents 1050 to generatetime-series event data from network packets captured by remote captureagents 1050.

GUI 1025 may include a number of features and/or mechanisms forfacilitating the management of multiple event streams. First, GUI 1025may group the event streams by one or more event stream attributesassociated with the event streams. For example, GUI 1025 may allow auser to specify an event stream attribute such as a category of an eventstream, a protocol used by network packets from which the event streamis generated, an application used to create the event stream, and/or anevent stream lifecycle of the event stream. GUI 1025 may display eventstream information for one or more subsets of the event streamsrepresented by the grouping of the event streams by the specified eventstream attribute. GUI 1025 may also group the event stream informationby multiple event stream attributes. For example, GUI 1025 may group theevent stream information by a first event stream attribute such asapplication, category, or protocol. GUI 1025 may then apply a secondgrouping of the event stream information by permanent or ephemeral eventstream lifecycles. As a result, a user may view permanent event streamsassociated with a given category, application, or protocol separatelyfrom ephemeral event streams associated with the category, applicationor protocol. Grouping and managing event streams is described in furtherdetail below with respect to FIGS. 17A-17C.

Second, GUI 1025 may display, along with the grouped event streaminformation, graphs of metrics associated with time-series event data inthe event streams. For example, GUI 1025 may include a sparkline ofnetwork traffic over time for each event stream under a given groupingof event streams. GUI 1025 may also show a sparkline of aggregatenetwork traffic and/or another aggregated metric for all event streamslisted under the grouping. In-line visualizations of metrics related toevent streams and/or captured network data is described in furtherdetail below with respect to FIGS. 17A-17B.

Third, GUI 1025 may enable the management of ephemeral event streams.For example, GUI 1025 may allow a user to create a new ephemeral eventstream, disable an existing event stream, delete an existing eventstream, and/or modify an end time for terminating an existing eventstream. Managing ephemeral event streams generated from captured networkdata is described in further detail below with respect to FIG. 17C.

Finally, GUI 1025 may provide bidirectional linking of ephemeral eventstreams to creators of the ephemeral event streams. For example, GUI1025 may include a hyperlink from event stream information for anephemeral event stream to creation information for a creator of theephemeral event stream, such as an application and/or capture triggerused to create the ephemeral event stream. GUI 1025 may also includeanother hyperlink from the creation information to the event streaminformation to facilitate understanding and analysis related to thecontext under which the ephemeral event stream was generated.Bidirectional linking of ephemeral event streams to creators of theephemeral event streams is described in further detail below withrespect to FIGS. 17C-17D.

Finally, configuration server 1020 may provide a risk-identificationmechanism 1007 for identifying a security risk from time-series eventdata generated by remote capture agents 1050, as well as a capturetrigger 1009 for generating additional time-series event data based onthe security risk. For example, risk-identification mechanism 1007 mayallow a user to view and/or search for events that may representsecurity risks through GUI 1025. Risk-identification mechanism 1007and/or GUI 1025 may also allow the user to set and/or activate capturetrigger 1009 based on the events shown and/or found throughrisk-identification mechanism 1007 and/or GUI 1025.

In particular, risk-identification mechanism 1007 and/or GUI 1025 mayallow the user to manually activate capture trigger 1009 afterdiscovering a potential security risk. In turn, the activated capturetrigger 1009 may modify configuration information in configurationcomponent 1004 that is propagated to remote capture agents 1050 totrigger the capture of additional network data by remote capture agents1050.

Alternatively, risk-identification mechanism 1007 may allow the user tocreate a search and/or recurring search for time-series event data thatmay match a security risk. If the search and/or recurring search findstime-series event data that matches the security risk, capture trigger1009 may automatically be activated to enable the generation ofadditional time-series event data, such as event data containing one ormore attributes associated with one or more protocols that facilitateanalysis of the security risk. Such automatic activation of capturetrigger 1009 may allow the additional event data to be generatedimmediately after a notable event is detected, thus averting the loss ofcaptured network data that results from enabling additional network datacapture only after a potential security risk is manually identified(e.g., by an analyst). Triggering the generation of additionaltime-series event data from network packets on remote agents based onpotential security risks is described in further detail below withrespect to FIGS. 12A-12B.

FIG. 11A shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 11A shows a screenshot of a GUI,such as GUI 1025 of FIG. 10. As described above, the GUI may be used toobtain configuration information that is used to configure thegeneration of event streams containing time-series event data at one ormore remote capture agents distributed across a network.

As shown in FIG. 11A, the GUI includes a table with a set of columns1102-1108 containing high-level information related to event streamsthat may be created using the configuration information. Each row of thetable may represent an event stream, and rows of the table may be sortedby column 1102.

Column 1102 shows an alphabetized or otherwise ordered or unordered listof names of the event streams, and column 1104 provides descriptions ofthe event streams. For example, columns 1102-1104 may include names anddescriptions of event streams generated from HTTP, Dynamic HostConfiguration Protocol (DHCP), DNS, FTP, email protocols, databaseprotocols, NFS, Secure Message Block (SMB), security protocols, SessionInitiation Protocol (SIP), TCP, and/or UDP network traffic. Columns1102-1104 may thus indicate that event streams may be generated based ontransport layer protocols, session layer protocols, presentation layerprotocols, and/or application layer protocols.

A user may select a name of an event stream under column 1102 to accessand/or update configuration information for configuring the generationof the event stream. For example, the user may select “DemoHTTP” incolumn 1102 to navigate to a screen of the GUI that allows the user tospecify event attributes, filters, and/or aggregation informationrelated to creating the “DemoHTTP” event stream, as discussed in furtherdetail below with respect to FIGS. 11B-11E.

Column 1106 specifies whether each event stream is enabled or disabled.For example, column 1106 may indicate that the “AggregateHTTP,”“DemoHTTP,” “dns,” “ftp,” “mysql-query,” “sip,” “tcp,” and “udp” eventstreams are enabled. If an event stream is enabled, time-series eventdata may be included in the event stream based on the configurationinformation for the event stream.

Column 1108 specifies whether each event stream is cloned from anexisting event stream. For example, column 1108 may indicate that the“AggregateHTTP” and “DemoHTTP” event streams have been cloned (e.g.,copied) from other event streams, while the remaining event streams maybe predefined with default event attributes.

The GUI also includes a user-interface element 1110 (e.g., “CloneStream”). A user may select user-interface element 1110 to create a newevent stream as a copy of an event stream listed in the GUI. Afteruser-interface element 1110 is selected, an overlay may be displayedthat allows the user to specify a name for the new event stream, adescription of the new event stream, and an existing event stream fromwhich the new event stream is to be cloned. The new event stream maythen be created with the same event attributes and/or configurationoptions as the existing event stream, and the user may use the GUI tocustomize the new event stream as a variant of the existing event stream(e.g., by adding or removing event attributes, filters, and/oraggregation information).

FIG. 11B shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 11B shows a screenshot of the GUIof FIG. 11A after the user has selected “DemoHTTP” from column 1102. Inresponse to the selection, the GUI displays configuration informationand/or configuration options for the “DemoHTTP” event stream.

Like the GUI of FIG. 11A, the GUI of FIG. 11B may include a table. Eachrow in the table may represent an event attribute that is eligible forinclusion in the event stream. For example, an event attribute may beincluded in the table if the event attribute can be obtained fromnetwork packets that include the protocol of the event stream. Columns1112-1120 of the table may allow the user to use the event attributes togenerate time-series event data that is included the event stream.First, column 1112 includes a series of checkboxes that allows the userto include individual event attributes in the event stream or excludethe event attributes from the event stream. If a checkbox is checked,the corresponding event attribute is added to the event stream, and therow representing the event attribute is shown with other included eventattributes in an alphabetized list at the top of the table. If acheckbox is not checked, the corresponding event attribute is omittedfrom the event stream, and the row representing the event attribute isshown with other excluded event attributes in an alphabetized listfollowing the list of included event attributes. Those skilled in theart will appreciate that the GUI may utilize other sortings and/orrankings of event attributes in columns 1112-1120.

Columns 1114-1118 may provide information related to the eventattributes. Column 1114 may show the names of the event attributes,column 1116 may provide a description of each event attribute, andcolumn 1118 may provide a term representing the event attribute. Inother words, columns 1114-1118 may allow the user to identify the eventattributes and decide whether the event attributes should be included inthe event stream.

Column 1120 may include a series of links labeled “Add.” The user mayselect one of the links to access a portion of the GUI that allows theuser to set a filter for the corresponding event attribute. The filtermay then be used in the generation of the event stream from networkdata. Creation of filters for generating event streams from networkpackets is described in further detail below with respect to FIGS.11D-11E.

The GUI of FIG. 11B also includes information 1122 related to the eventstream. For example, information 1122 may include the name (e.g.,“DemoHTTP”) of the event stream, the protocol classification and/or type(e.g., “http.event”) of the event stream, and the number of filters(e.g., “0 filters configured”) set for the event stream. Information1122 may also include a checkbox 1136 that identifies if the eventstream contains aggregated event data. If checkbox 1136 is checked, theGUI may be updated with options associated with configuring thegeneration of an aggregated event stream, as described below withrespect to FIG. 11C.

Finally, the GUI of FIG. 11B includes a set of user-interface elements1124-1134 for managing the event stream. First, the user may selectuser-interface element 1124 (e.g., “Enabled”) to enable generation ofthe event stream from network data and user-interface element 1126(e.g., “Disabled”) to disable the generation of the event stream fromthe network data.

Next, the user may select user-interface element 1128 (e.g., “Clone”) toclone the event stream and user-interface element 1130 (e.g., “Delete”)to delete the event stream. If the user selects user-interface element1128, the GUI may obtain a name and description for the cloned eventstream from the user. Next, the GUI may copy the content of columns1112-1120, including configuration options (e.g., checkboxes in column1112 and filters added using links in column 1120) that have beenchanged but not yet saved by the user, to a new screen for configuringthe generation of the cloned event stream.

If the user selects user-interface element 1130, the GUI may remove theevent stream from the table in FIG. 11A. In turn, a representation ofthe event stream may be removed from the configuration information tostop the generation of time-series event data in the event stream by oneor more remote capture agents.

The user may select user-interface element 1132 (e.g., “Cancel”) todischarge changes to the configuration information made in the currentscreen of the GUI. Conversely, the user may select user-interface 1134(e.g., “Save”) to propagate the changes to the configurationinformation, and in turn, update the generation of event data fromnetwork packets captured by the remote capture agents based on thechanges.

FIG. 11C shows an exemplary screenshot in accordance with the disclosedembodiments. In particular, FIG. 11C shows a screenshot of the GUI ofFIG. 11B after checkbox 1136 has been checked. Because checkbox 1136 ischecked, the GUI includes a number of user-interface elements forconfiguring the generation of an aggregated event stream. The aggregatedevent stream may include aggregated event data, which in turn may begenerated by aggregating and/or extracting event attributes from one ormore network packets in a packet flow. For example, an HTTP event may begenerated from one to several HTTP packets representing an HTTPrequest/response pair. Event attributes from multiple HTTP events maythen be aggregated into a single aggregated HTTP event to reduce theamount of event data generated from the network data without losingimportant attributes of the event data.

As shown in FIG. 11C, a new column 1138 is added to the table. Each rowin column 1138 may include a pair of user-interface elements (e.g.,buttons) that allow the user to identify the corresponding eventattribute as a key attribute or an aggregation attribute. One or morekey attributes may be used to generate a unique key representing eachaggregated event, and one or more aggregation attributes may beaggregated prior to inclusion in the aggregated event. Some eventattributes (e.g., “dest_ip,” “src_ip,” “uri_path”) may only be used askey attributes because the event attributes are not numeric in nature.On the other hand, event attributes that may be summed (e.g.,“dest_port,” “status,” “bytes,” “bytes_in,” “bytes_out,” “time taken”)may have numeric values.

Event attributes identified as key attributes in column 1138 may besorted at the top of the table, followed by event attributes identifiedas aggregation attributes. Event attributes that are not included in theevent stream (e.g., event attributes with unchecked checkboxes in column1112) may be shown below the aggregation attributes in the table.Alternatively, event attributes may be displayed in the table accordingto other sortings and/or rankings.

While sums are the only type of aggregation shown in the GUI of FIG.11C, other types of aggregation may also be used to generate aggregatedevent data. For example, aggregated event streams may be created usingminimums, maximums, averages, standard deviations, and/or other summarystatistics of event attributes.

The GUI of FIG. 11C also includes a user-interface element 1140 (e.g., atext box) for obtaining an aggregation interval over which eventattributes are to be aggregated into a single aggregated event. Theaggregation interval may be increased to increase the amount ofaggregation in the aggregated event stream and reduced to decrease theamount of aggregation in the aggregated event stream.

For example, column 1138 may indicate that the “dest_p,” “dest_port,”“src_ip,” “status,” and “uri_path” event attributes are specified as keyattributes and the “bytes,” “bytes_in,” “bytes_out,” and “time taken”event attributes are specified as aggregation attributes. Similarly, anaggregation interval of 60 seconds may be obtained from user-interfaceelement 1140. As a result, the aggregated event stream may includeaggregated events generated from event data over a 60-second interval.After each 60-second interval has passed, a separate aggregated eventwith a unique key may be generated for each unique combination of“dest_ip,” “dest_port,” “src_ip,” “status,” and “uri_path” keyattributes encountered during the interval. Values of “bytes,”“bytes_in,” “bytes_out,” and “time taken” for events within the intervalthat match the unique combination of key attributes may also be summedand/or otherwise aggregated into the aggregated event. Aggregated eventsgenerated from the configuration options may then be shown in the sameGUI, as described in further detail below with respect to FIG. 11F.

Such configuration of event streams and/or aggregated event streams mayallow network data to be captured at different levels of granularityand/or for different purposes. For example, an aggregated event streammay include all possible event attributes for the event stream to enableoverall monitoring of network traffic. On the other hand, one or moreunaggregated event streams may be created to capture specific types ofnetwork data at higher granularities than the aggregated event stream.In addition, multiple event streams may be created from the same packetflow and/or event data to provide multiple “views” of the packet flowand/or event data.

FIG. 11D shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 11D shows a screenshot of the GUIof FIGS. 11B-11C after an “Add” link in column 1120 is selected. Forexample, the GUI of FIG. 11D may be shown as an overlay on the screensof FIGS. 11B-11C to enable the addition of filters to configurationinformation for the event stream(s) and/or aggregated event stream(s)shown on the screens.

As with the screenshots of FIGS. 11A-11C, the GUI of FIG. 11D includesinformation and/or user-interface elements organized into a table. Rowsof the table may represent filters for an event stream and/or aggregatedevent stream, and columns 1142-1150 of the table may facilitateidentification and/or configuration of the filters.

First, column 1142 may provide a list of terms representing eventattributes to which the filters are to be applied. For example, column1142 may specify an “http.status” term representing the “status” eventattribute and an “http.uri-stem” term representing the “uri_path” eventattribute.

Column 1144 may be used to provide a comparison associated with eachfilter. For example, a user may select a cell under column 1144 toaccess a drop-down menu of possible comparisons for the correspondingfilter. As shown in FIG. 11D, the second cell of column 1144 is selectedto reveal a drop-down menu of comparisons for a string-based eventattribute (e.g., “uri_path”). Within the drop-down menu, “RegularExpression” is selected, while other options for the comparison mayinclude “False,” “True,” “Is defined,” “Is not defined,” “Not RegularExpression,” “Exactly matches,” “Does not exactly match,” “Contains,”“Does not contain,” “Starts with,” “Does not start with,” “Ends with,”“Does not end with,” “Ordered before,” “Not ordered before,” “Orderedafter,” and “Not ordered after.” As a result, a number of comparisonsmay be made with string-based event attributes during filtering ofnetwork data by the string-based event attributes.

Column 1146 may allow the user to specify a value against which thecomparison in column 1144 is made. Cells in column 1146 may betext-editable fields and/or other user-interface elements that acceptuser input. For example, the second cell of column 1146 may include avalue of “admin” that is entered by the user. Consequently, the valuesin the second cells of columns 1144-1146 may be used to generate afilter that determines if the “uri_path” event attribute from networkdata matches a regular expression of “admin.” If the network datamatches the regular expression, the network data may be used to generateevent data, which may subsequently be used to generate aggregated eventdata. If the network data does not match the regular expression,generation of event data from the network data may be omitted.

Column 1148 may include a set of checkboxes with a “Match All” header.The user may check a checkbox in column 1148 to require each value in amulti-value event attribute to match the filter. For example, the usermay check a checkbox in column 1148 for a filter that is applied to achecksum event attribute to ensure that each of multiple checksums in agiven network packet and/or event satisfies the comparison in thefilter.

Column 1150 may allow the user to delete filters from the configurationinformation. For example, the user may select a user-interface (e.g., anicon) in a cell of column 1150 to remove the corresponding filter fromthe configuration information.

The GUI also includes a set of user-interface elements 1152-1154 fordetermining the applicability of individual filters or all filters tothe network data. For example, the user may select user-interfaceelement 1152 (e.g., “All”) to apply the filters so that only data thatmatches all filters in the table is used to generate events. Conversely,the user may select user-interface element 1154 (e.g., “Any”) to applythe filters so that data matching any of the filters in the data is usedto generate events. In other words, user-interface element 1152 may beselected to apply a logical conjunction to the filters, whileuser-interface element 1154 may be selected to apply a logicaldisjunction to the filters.

FIG. 11E shows an exemplary screenshot in accordance with the disclosedembodiments. As with the screenshot of FIG. 11D, FIG. 11E shows a GUIfor adding and/or managing filters for generating event data at one ormore remote capture components.

Within the GUI of FIG. 11E, the first cell of column 1144 is selected.In turn, a drop-down menu of possible comparisons is shown for thecorresponding filter. Because the filter relates to a numeric eventattribute (e.g., an HTTP status code), comparisons in column 1144 may benumeric in nature. For example, the “Greater than” comparison isselected, while other possible comparisons may include “False,” “True,”“Is defined,” “Is not defined,” “Equals,” “Does not equal,” “Less than,”“Greater than or equal to,” and “Less than or equal to.” The differencesin comparisons shown in FIG. 11E and FIG. 11D may ensure thatcomparisons that are meaningful and/or relevant to the types of eventattributes specified in the filters are used with the filters.

FIG. 11F shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 11F shows a screenshot of a GUI,such as GUI 1025 of FIG. 10. The GUI of FIG. 11F may provide informationrelated to aggregated events, such as aggregated events generated usingthe GUI of FIG. 11C.

As shown in FIG. 11F, a first column 1156 contains a timestamp of anaggregated event, and a second column 1158 shows the aggregated event.Within column 1158, the aggregated event includes a number of eventattributes. Some of the event attributes (e.g., “dest_ip,” “dest_port,”“src_ip,” “status,” “uri_path”) are key attributes that are used touniquely identify the aggregated event, and other event attributes(e.g., “dest_port,” “status,” “bytes,” “bytes_in,” “bytes_out,” “timetaken”) may be numerically summed before the event attributes areincluded in the aggregated event.

FIG. 12A shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 12A shows a screenshot of a GUI,such as GUI 1025 of FIG. 10. The GUI may be used with arisk-identification mechanism and/or a capture trigger, such asrisk-identification mechanism 1007 and capture trigger 1009 of FIG. 10.

The GUI of FIG. 12A may include a portion 1202 that represents therisk-identification mechanism. For example, portion 1202 may display adashboard of time-series event data that represents security risks. Thedashboard includes a number of potential security risks, such as “HTTPErrors,” “DNS Errors,” “Cloud Email,” “NFS Activity,” and “Threat ListActivity.” Events that match one of the listed potential security risksmay be represented as bars within a time interval represented by thehorizontal dimension of the dashboard. For example, a security risk 1206may be shown as a series of bars clustered around an interval of timeunder “DNS Errors” in portion 1202.

On the other hand, the dashboard may lack data for other potentialsecurity risks because the data volume associated with capturing networkdata across all protocols and/or security risks may be too large toeffectively store and/or consume. As a result, portion 1202 may indicatethat no data is available (e.g., “Search returned no results”) for the“HTTP Errors,” “Cloud Email,” “NFS Activity,” and “Threat List Activity”security risks.

The GUI may also include a portion 1204 that represents a capturetrigger for generating additional time-series event data based onidentified security risks from portion 1202. For example, portion 1204may include a checkbox that allows a user to activate the capturetrigger upon identifying security risk 1206 in portion 1202. Portion1204 may also include a first drop-down menu that allows the user tospecify one or more protocols (e.g., “HTTP,” “DNS,” “All Email,”“NFS/SMB,” “All Protocols”) of additional time-series event data to becaptured with the capture trigger. Portion 1204 may additionally includea second drop-down menu that allows the user to specify a period (e.g.,“4 Hours”) over which the additional time-series event data is to becaptured after the capture trigger is activated.

After the capture trigger is activated, configuration information on oneor more remote capture agents used to generate the time-series eventdata may be updated to include the additional protocol(s) specified inportion 1204. For example, configuration information for configuring thegeneration of additional event streams from the specified protocol(s)may be propagated to the remote capture agents, and the remote captureagents may use the configuration to create the event streams fromnetwork data and/or event data at the remote capture agents. Theconfiguration information may include default event attributes for theprotocol(s) and/or event attributes that may be of interest to thesecurity assessment of network packet flows. For example, theconfiguration information may specify the generation of event datarelated to other security risks, such as the security risks shown in thedashboard. Once the event data is generated and/or indexed, the eventdata may be shown in the dashboard to facilitate verification,monitoring, and/or analysis of the security risk. After thepre-specified period obtained from portion 1204 has passed, theconfiguration information on the remote capture agents may be updated todisable the generation of the additional event streams and reduce thevolume of network data captured by the remote capture agents.

As with the user interfaces of FIGS. 11A-11E, the user may add one ormore filters that are applied during the generation of the additionaltime-series event data. For example, the user may use the userinterfaces of FIGS. 11D-11E to add a filter for network and/or eventdata that exactly matches the IP address (e.g., 10.160.26.206) fromwhich the security risk was detected. As a result, the additionaltime-series data may be generated only from network data containing thesame source IP address. The user may also use the user interfaces ofFIGS. 11A-11C to customize the collection of additional time-seriesevent data by protocol and/or event attributes.

FIG. 12B shows an exemplary screenshot in accordance with the disclosedembodiments. In particular, FIG. 12B shows a screenshot of a GUI, suchas GUI 1025 of FIG. 10. Like the GUI of FIG. 12A, the GUI of FIG. 12Bincludes a first portion 1206 representing a risk-identificationmechanism and a second portion 1208 representing a capture trigger.

Portion 1206 may allow a user to create a recurring search fortime-series event data that matches a security risk. For example,portion 1206 may include user-interface elements for obtaining a domain,application context, description, search terms, time range (e.g., startand end times), and/or frequency (e.g., daily, hourly, every fiveminutes, etc.) for the recurring search. The user may use theuser-interface elements of portion 1206 to specify a recurring searchfor an excessive number of failed login attempts in captured networkand/or event data, which may represent brute force access behavior thatconstitutes a security risk.

Portion 1208 may allow the user to provide the capture trigger, which isautomatically activated if the recurring search finds time-series eventdata that matches the security risk. As with portion 1204 of FIG. 12A,portion 1208 may allow the user to set the capture trigger, specify oneor more protocols to be captured with the capture trigger, and/or apre-specified period over which network data using the protocol(s) is tobe captured.

After the user has finished defining the recurring search and capturetrigger, the user may select a user-interface 1210 (e.g., “Save”) tosave the recurring search and capture trigger. The capture trigger maythen be activated without additional input from the user once aniteration of the recurring search identifies the security risk.Conversely, the user may select a user-interface element 1212 (e.g.,“Cancel”) to exit the screen of FIG. 12B without creating the recurringsearch and/or capture trigger.

FIG. 13 shows a flowchart illustrating the processing of network data inaccordance with the disclosed embodiments. In one or more embodiments,one or more of the steps may be omitted, repeated, and/or performed in adifferent order. Accordingly, the specific arrangement of steps shown inFIG. 13 should not be construed as limiting the scope of theembodiments.

Initially, configuration information is obtained at a remote captureagent from a configuration server over a network (operation 1302). Theremote capture agent may be located on a separate network from that ofthe configuration server. For example, the remote capture agent may beinstalled on a physical and/or virtual machine on a remote networkand/or cloud. As discussed above, the remote capture agent and otherremote capture agents may be used to capture network data from a set ofremote networks in a distributed manner.

Next, the configuration information is used to configure the generationof event data from network packets captured by the remote capture agentduring the runtime of the remote capture agent (operation 1304). Forexample, the configuration information may be used to configure theremote capture agent to identify certain types of network packets,extract network data from the network packets, and/or include thenetwork data in the event data.

The remote capture agent may identify network packets in a packet flowbased on control information in the network packets (operation 1306).For example, network packets between a source and destination may beidentified based on source and/or destination network addresses, sourceand/or destination ports, and/or transport layer protocols in theheaders of the network packets.

The remote capture agent may also assemble the packet flow from thenetwork packets (operation 1308) and/or decrypt the network packets upondetecting encryption of the network packets (operation 1310). Forexample, the remote capture agent may rearrange out-of-order TCP packetsinto a TCP stream. The remote capture agent may also analyze the bytesignatures of the network packets' payloads to identify encryption ofthe network packets and use an available private key to decrypt thenetwork packets.

After the packet flow is identified, assembled and/or decrypted, theremote capture agent may obtain a protocol classification for the packetflow (operation 1312). For example, the remote capture agent may providenetwork packets in the packet flow to a protocol-decoding mechanism andreceive one or more protocol identifiers representing the protocols usedby the network packets from the protocol-decoding mechanism.

Next, the remote capture agent may use configuration informationassociated with the protocol classification to build an event streamfrom the packet flow (operation 1314), as described in further detailbelow with respect to FIG. 14. The remote capture agent may thentransmit the event stream over a network for subsequent storage andprocessing of the event stream by one or more components on the network(operation 1316). For example, the remote capture agent may transmit theevent stream to one or more data storage servers, configuration servers,and/or indexers on the network.

An update to the configuration information may be received (operation1316). For example, the remote capture agent may receive an update tothe configuration information after the configuration information ismodified at a configuration server. If an update to the configurationinformation is received, the update is used to reconfigure thegeneration of time-series event data at the remote capture agent duringruntime of the remote capture agent (operation 1320). For example, theremote capture agent may be use the updated configuration information togenerate one or more new event streams, discontinue the generation ofone or more existing event streams, and/or modify the generation of oneor more existing event streams.

The remote capture agent may continue to be used (operation 1322) tocapture network data. If the remote capture agent is to be used, packetflows captured by the remote capture agent are identified (operation1306), and network packets in the packet flows are assembled into thepacket flows and/or decrypted (operations 1308-1310). Protocolclassifications for the packet flows are also obtained and used, alongwith configuration information associated with the protocolclassifications, to build event streams from the packet flows(operations 1312-1314). The event streams are then transmitted over thenetwork (operation 1316), and any updates to the configurationinformation are used to reconfigure the operation of the remote captureagent (operations 1318-1320) during generation of the event streams.Capture of network data by the remote capture agent may continue untilthe remote capture agent is no longer used to generate event data fromnetwork data.

FIG. 14 shows a flowchart illustrating the process of usingconfiguration information associated with a protocol classification tobuild an event stream from a packet flow in accordance with thedisclosed embodiments. In one or more embodiments, one or more of thesteps may be omitted, repeated, and/or performed in a different order.Accordingly, the specific arrangement of steps shown in FIG. 14 shouldnot be construed as limiting the scope of the embodiments.

First, one or more event attributes associated with the protocolclassification are obtained from the configuration information(operation 1402). For example, the event attribute(s) may be obtainedfrom a portion of the configuration information that specifies thegeneration of an event stream from network data matching the protocolclassification.

Next, the event attribute(s) are extracted from network packets in thepacket flow (operation 1404). For example, the event attribute(s) may beused to generate event data from the network packets. The configurationinformation may optionally be used to transform the extracted eventattribute(s) (operation 1406). For example, the configurationinformation may be used to aggregate the event data into aggregatedevent data that reduces the volume of event data generated whileretaining the important aspects of the event data.

Finally, the extracted and/or transformed event attributes are includedin the event stream (operation 1408). For example, the event stream maybe include a series of events and/or aggregated events that containevent attributes that are relevant to the protocol classification of thenetwork packets represented by the events.

FIG. 15 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.In one or more embodiments, one or more of the steps may be omitted,repeated, and/or performed in a different order. Accordingly, thespecific arrangement of steps shown in FIG. 15 should not be construedas limiting the scope of the embodiments.

First, a GUI for obtaining configuration information for configuring thegeneration of time-series event data from network packets captured byone or more remote agents is provided (operation 1502). The GUI mayinclude a number of user-interface elements for streamlining thecreation and/or update of the configuration information. First, the GUImay provide a set of user-interface elements for including one or moreevent attributes in the time-series event data of an event streamassociated with a protocol classification of the network packets(operation 1504). For example, the GUI may include a set of checkboxesthat enable the selection of individual event attributes for inclusionin the time-series event data.

Second, the GUI may provide a set of user-interface elements formanaging the event stream (operation 1506) and/or obtaining the protocolclassification for the event stream. For example, the GUI may includeone or more user-interface elements for cloning the event stream from anexisting event stream, which imparts the protocol classification of theexisting event stream on the cloned event stream. The GUI may alsoinclude user-interface elements for deleting the event stream, enablingthe event stream, and/or disabling the event stream.

Third, the GUI may provide a set of user-interface elements forfiltering the network packets (operation 1508) prior to generating thetime-series event data from the network packets. Each filter mayidentify an event attribute, a comparison to be performed on the eventattribute, and/or a value to which the event attribute is to becompared. For example, the filter may match the event attribute to aBoolean value (e.g., true or false), perform a numeric comparison (e.g.,equals, greater, less than, greater than or equal to, less than or equalto), and/or verify the definition of (e.g., the existence of) the eventattribute in network data. The filter may also compare the eventattribute to a regular expression, perform an exact match of the eventattribute to the value, perform a partial match of the event attributeto the value, and/or determine the event attribute's position in anordering.

Fourth, the GUI may provide a set of user-interface elements foraggregating the event attribute(s) into aggregated event data that isincluded in the event stream (operation 1510). For example, the GUI mayprovide user-interface elements for identifying event attributes as keyattributes used to generate a key representing the aggregated event dataand/or aggregation attributes to be aggregated prior to inclusion in theaggregated event data. The GUI may also include one or moreuser-interface elements for obtaining an aggregation interval over whichthe one or more event attributes are aggregated into the aggregatedevent data.

Finally, the event attribute(s), protocol classification, filteringinformation, and/or aggregation information obtained from the GUI areincluded in the configuration information (operation 1512). Theconfiguration information may then be used to configure theprotocol-based capture, filtering, and/or aggregation of network data atthe remote capture agent(s).

FIG. 16 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.In one or more embodiments, one or more of the steps may be omitted,repeated, and/or performed in a different order. Accordingly, thespecific arrangement of steps shown in FIG. 16 should not be construedas limiting the scope of the embodiments.

Initially, a risk-identification mechanism for identifying a securityrisk from time-series event data generated from network packets capturedby one or more remote capture agents distributed across a network isprovided (operation 1602). The risk-identification mechanism may includea GUI that displays an event of interest related to the security risk.For example, the GUI may show potential security risks in a dashboardand/or other visualization of the time-series event data. Alternatively,the risk-identification mechanism may include a search and/or recurringsearch for a subset of the time-series event data matching the securityrisk. For example, the risk-identification mechanism may include asearch mechanism that allows a user to search for threats, attacks,errors, and/or other notable events in the time-series event data.

Next, a capture trigger for generation additional time-series data fromthe network packets on the remote capture agent(s) based on the securityrisk is provided (operation 1604). The capture trigger may be receivedthrough one or more user-interface elements of a GUI, such as the sameGUI used to provide the risk-identification mechanism. For example, thecapture trigger may be activated in a portion of the GUI that is above,below, and/or next to a dashboard that displays security risks to theuser. Alternatively, the capture trigger may be linked to a recurringsearch for time-series event data that matches a security risk. As aresult, the capture trigger may automatically be activated oncetime-series event data matching the security risk is found.

After the capture trigger is activated, the capture trigger is used toconfigure the generation of the additional time-series event data fromthe network packets (operation 1606). For example, activation of thecapture trigger may result in the updating of configuration informationfor the remote capture agent(s), which causes the remote captureagent(s) to generate additional event streams containing eventattributes associated with protocols that facilitate analysis of thesecurity risk.

Finally, generation of the additional time-series event data is disabledafter a pre-specified period has passed (operation 1608). For example,generation of the additional time-series event data may be set to expirea number of hours or days after the capture trigger is activated. Theexpiry may be set by the user and/or based on a default expiration forsecurity-based capture of additional network data from network packets.

FIG. 17A shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 17A shows a screenshot of a GUI,such as GUI 1025 of FIG. 10. As described above, the GUI may be used toobtain configuration information that is used to configure thegeneration of event streams containing time-series event data at one ormore remote capture agents distributed across a network.

As shown in FIG. 17A, the GUI includes a table with a set of columns1708-1724. Columns 1710-1718 may include high-level event streaminformation related to event streams that are created and/or managedusing the configuration information. Each row of the table may representan event stream, and rows of the table may be sorted by column 1710.

Column 1710 shows an alphabetized list of names of the event streams,and column 1714 may specify a protocol associated with each eventstream. For example, columns 1710 and 1714 may include names and/orprotocols of event streams generated from HTTP, Dynamic HostConfiguration Protocol (DHCP), DNS, FTP, email protocols, databaseprotocols, NFS, Secure Message Block (SMB), security protocols, SessionInitiation Protocol (SIP), TCP, and/or UDP network traffic. In otherwords, the event streams may be generated based on transport layerprotocols, session layer protocols, presentation layer protocols, and/orapplication layer protocols.

A user may select a name of an event stream under column 1710 to accessand/or update configuration information for configuring the generationof the event stream. For example, the user may select “Stream_A” incolumn 1710 to navigate to a screen of the GUI that allows the user tospecify event attributes, filters, and/or aggregation informationrelated to creating the “Stream_A” event stream.

Column 1712 specifies a type indicating whether each event stream iscloned from an existing event stream. For example, column 1712 mayindicate that the “Stream_C” and “Stream_D” streams have been cloned(e.g., copied) from other event streams, while the remaining eventstreams may be predefined with default event attributes.

Column 1716 shows an application associated with each event stream, andcolumn 1718 includes a description of each event stream. For example,column 1716 may include the names of applications used to create theevent streams (e.g., “Stream,” “Enterprise Security,” etc.), and column1718 may include descriptions that are generated by the applicationsand/or users of the applications.

The table may also include a column 1722 that specifies a statusindicating whether each event stream is enabled or disabled. Forexample, column 1722 may indicate that the “Stream_A,” “Stream_B,”“Stream_C,” and “Stream_E” event streams are enabled. If an event streamis enabled, time-series event data may be included in the event streambased on the configuration information for the event stream. If an eventstream is disabled, time-series event data may not be generated for theevent stream.

Event streams in the table may further be sorted by information in othercolumns of the table and/or randomly. For example, the user may selectthe column header of a given column (e.g., columns 1708-1724) to orderthe displayed event stream information by the information represented bythe column. Alternatively, event streams in the table may be randomlysorted and/or sorted by an attribute that is not explicitly displayed ina column of the table.

The GUI also includes a user-interface element 1730 (e.g., “CloneStream”). A user may select user-interface element 1730 to create a newevent stream as a copy of an event stream listed in the GUI. Afteruser-interface element 1730 is selected, an overlay may be displayedthat allows the user to specify a name for the new event stream, adescription of the new event stream, and an existing event stream fromwhich the new event stream is to be cloned. The new event stream maythen be created with the same event attributes and/or configurationoptions as the existing event stream, and the GUI may navigate the userto a new screen for customizing the new event stream as a variant of theexisting event stream (e.g., by adding or removing event attributes,filters, and/or aggregation information).

As mentioned above, the GUI may include functionality to group eventinformation for the event streams by one or more event streamattributes. In particular, the GUI may provide a user-interface element1704 for specifying an event stream attribute by which event streams areto be grouped. For example, user-interface element 1704 may be adrop-down menu that allows the user to specify grouping of the eventstreams by “Protocol,” “Category,” or “Apps” (e.g., applications). Asshown in FIG. 17A, the “Protocol” event stream attribute is specified inuser-interface element 1704. In response to the selection of “Protocol”in user-interface element 1704, the GUI may display a list 1702 ofpossible values for the “Protocol” event stream attribute. For example,list 1702 may include protocols such as HTTP, FTP, TCP, UDP, and SMTP.

The user may select a protocol name from list 1702 to view event streaminformation for a subset of the event streams matching the protocol(e.g., event streams containing time-series event data generated fromnetwork packets classified as using the protocol). Because “HTTP” isselected in list 1702, the table may show event stream information forevent streams that match the “HTTP” protocol classification, asindicated in column 1714. The user may select another protocol name fromlist 1702 to view event streams associated with another protocolrepresented by the protocol name, or the user may select “All” in list1702 to view all event streams, regardless of the event streams'protocol classifications.

The GUI may additionally group the event stream information by an eventstream lifecycle of the event streams. In particular, the GUI mayinclude two user-interface elements 1766-1768 for specifying an eventstream lifecycle. The user may select user-interface element 1766 (e.g.,“Permanent”) to view event stream information for permanent eventstreams and user-interface element 1768 (e.g., “Ephemeral”) to viewevent stream information for ephemeral event streams. Selection of oneuser-interface element 1766-1768 may result in the automatic deselectionof the other user-interface element. In response to the selection ofeither user-interface element 1766 or user-interface element 1768, theGUI may further group event stream information shown in the table by theevent stream lifecycle represented by the selected user-interfaceelement. For example, the GUI may show only permanent event streams thatmatch the “HTTP” protocol classification in the table of FIG. 17Abecause user-interface element 1766 and “HTTP” are selected.

To further facilitate analysis and/or management of the event streams,column 1720 of the table may include graphs of metrics associated withthe event streams inline with event stream information for the eventstreams. The graphs may be generated using time-series event data forthe event streams. For example, column 1720 may show, for each eventstream represented by a row in the table, a sparkline of network trafficover time for the event stream. Alternatively, column 1720 may showgraphs and/or sparklines of other metrics, such as a number of eventsand/or a number of notable events over time. As with other columns inthe table, column 1720 may be updated based on user interaction withuser-interface elements 1704 and 1766-1768 and list 1702. For example,the selection of one or more other groupings using user-interfaceelements 1704 and 1766-1768 and list 1702 may trigger the display ofevent stream information and graphs in the table for event streamsmatching the other grouping(s).

The user may click on a graph in column 1720 to navigate to a screencontaining a larger version of the graph and one or more user-interfaceelements for changing a view of the graph. For example, after the userselects a graph in column 1720, the GUI may navigate the user to adashboard with a more detailed version of the graph that includes ascale and/or labeled axes. The dashboard may also include scrollbars,sliders, buttons, and/or other user-interface elements that allow theuser to change the scale along one or both axes, scroll across differentportions of the data (e.g., different time ranges), and/or view datafrom multiple event streams in the same graph.

The GUI may also include a user-interface element 1732 that shows anaggregated value of the metric in the graphs of column 1720. Forexample, user-interface element 1732 may include a sparkline ofaggregate network traffic, events, and/or notable events over time forthe event streams represented by the rows of the table. The aggregatemetric may be calculated as a sum, average, and/or other summarystatistic.

User-interface element 1732 may also display a numeric value of theaggregate metric (e.g., “154 Mb/s”) over the time spanned by thesparkline. For example, the numeric value shown to the left of thesparkline in user-interface element 1732 may represent a value ofaggregate network traffic at a time represented by a given point in thesparkline. The user may position a cursor at different points along thesparkline to view different values of the aggregate network trafficrepresented by the points. Similarly, the user may position the cursorat different points in the graphs of column 1720 to trigger the displayof numeric values of network traffic at times represented by thosepoints.

As with graphs in column 1720, the graph in user-interface element 1732may be generated or updated based on the event stream information shownin the table. For example, the selection of “UDP” in list 1702 may causethe GUI to display event stream information for event streams matchingthe “UDP” protocol classification. In turn, graphs in column 1720 anduser-interface element 1732 may be updated to reflect network traffic,events, notable events, network bandwidth, total bandwidth,protocol-based bandwidth, and/or other metrics associated with the “UDP”event streams.

The graphs in column 1720 and/or user-interface element 1732 may furtherbe updated in real-time with time-series event data as the time-seriesevent data is received from one more remote capture agents. For example,sparklines and/or other graphical representations in column 1720 anduser-interface element 1732 may shift as the time window spanned by thesparklines advances and additional time-series event data is collectedwithin the time window.

In addition to displaying event stream information and graphs for one ormore groupings of event streams, the GUI of FIG. 17A may enablemanagement of the event streams through a column 1724 that allows theuser to perform one or more actions on individual event streams. Eachrow of the table may include a user-interface element in column 1724that, when selected, activates a drop-down menu of possible actions tobe applied to the corresponding event stream.

Within the GUI, the user-interface element in the first row of column1724 may be selected. As a result, a drop-down menu may be displayedbelow the user-interface element with a set of options, including“Disable,” “Clone,” and “Delete.” The user may select “Disable” todisable generation of the event stream from network data. Alternatively,the “Disable” option may be replaced with an “Enable” option if theevent stream (e.g., “Stream_D”) is already disabled to allow the user toenable generation of the event stream from network data.

The user may select “Clone” to create a new event stream as a copy ofthe event stream. If “Clone” is selected, the GUI may obtain a name anddescription for the cloned event stream. The GUI may then copyconfiguration information for the event stream to a new screen forconfiguring the cloned event stream. As described in theabove-referenced application, configuration of new and/or cloned eventstreams may include one or more event attributes to be included in theevent stream, filtering network packets prior to generating the eventstream from the network packets, and/or aggregating the eventattribute(s) into aggregated event data that is included in the eventstream.

The user may select “Delete” to delete the event stream. If “Delete” isselected, the GUI may remove event stream information for the eventstream from the table. In turn, a representation of the event stream maybe removed from the configuration information to stop the generation oftime-series event data in the event stream by one or more remote captureagents.

The GUI may additionally include a user-interface element 1726 (e.g.,“Bulk Edit”) that allows the user to apply an action associated withmanaging an event stream to multiple event streams. The user may use aset of checkboxes in column 1708 to select one or more event streams towhich the action is to be applied. The user may then selectuser-interface element 1726 to access a drop-down menu containing a setof possible actions to apply to the selected event streams. For example,the drop-down menu may include options for enabling, disabling, anddeleting the selected event streams, which are similar to options in thedrop-down menu of user-interface elements in column 1724.

Finally, the GUI may include a user-interface element 1728 that allowsthe user to search for event streams. The user may type one or morekeywords into a text box provided by user-interface element 1728, andthe GUI may match the keyword(s) to the names, descriptions, and/orother event stream attributes of the event streams in the table. Eventstream information for event streams that do not match the keyword(s)may be removed from the table while the search is in effect.User-interface element 1728 may thus provide another mechanism by whichevent stream information in the table can be grouped and/or filtered.Consequently, the GUI of FIG. 17A may allow the user to create, find,and/or manage event streams across multiple applications, categories,keywords, and/or protocols that may be relevant to the user's interestsor goals.

FIG. 17B shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 17B shows the GUI of FIG. 17A afterthe event stream attribute in user-interface element 1704 is changedfrom “Protocol” to “Category.” In response to the change, the GUI mayupdate list 1702 with possible values for the “Category” event streamattribute. For example, list 1702 may include different technologicalcategories of network data represented by the event streams, such as“Infrastructure,” “Networking,” “File Transfer,” “Web,” and “Email.” Aswith the GUI of FIG. 17A, the user may select “All” in list 1702 to viewall event streams, regardless of the categories to which the eventstreams belong.

Other categories not shown in list 1702 may include, but are not limitedto, messaging, authentication, database, telephony, and/or networkmanagement. Finally, categories in list 1702 may include one or moreuser-created values. For example, the GUI may provide one or moreuser-interface elements that allow the user to specify a name of a newcategory, along with one or more event stream attributes of eventstreams to be included under the new category.

Within list 1702, “Networking” is selected. As a result, the table mayinclude names, types, protocols, applications, descriptions, and/orother event stream information for permanent event streams in anetworking category, such as event streams associated with networkingprotocols (e.g., DHCP, DNS, TCP, UDP). Sparklines in column 1720 anduser-interface element 1732 may also be updated to reflect metrics andan aggregated metric associated with the event streams represented bythe rows of the table in FIG. 17B, respectively.

FIG. 17C shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 17C shows the GUI of FIG. 17B afteruser-interface element 1768 has been selected and the event streamattribute in user-interface element 1704 is changed from “Category” to“Apps.” In the GUI of FIG. 17C, list 1702 includes possible values forthe “Apps” (e.g., applications) event stream attribute. For example,list 1702 may include names of applications (e.g., “Stream,” “EnterpriseSecurity”) associated with event streams, such as applications fromwhich the event streams were created. Within list 1702, “EnterpriseSecurity” is selected. The user may select another application name fromlist 1702 to view event streams associated with another applicationrepresented by the application name, or the user may select “All” inlist 1702 to view all event streams, regardless of the applicationsassociated with the event streams.

Because user-interface element 1768 is also selected, the table includesevent stream information for ephemeral event streams. In other words,the GUI may group event streams by application and event streamlifecycle so that event stream information for event streams that matchboth the “Enterprise Security” application name and the ephemeral eventstream lifecycle is shown in the table.

The user may apply an additional grouping or filter to event streaminformation shown in the table by performing a search usinguser-interface element 1728. For example, the user may type one or morekeywords into a text box provided by user-interface element 1728, andthe GUI may match the keyword(s) to the names, descriptions, and/orother event stream attributes of the ephemeral event streams in thetable. Event stream information for event streams that do not match thekeyword(s) may be removed from the table while the search is in effect.

As shown in FIG. 17C, the table includes a different set of columns1734-1750 from the table of FIGS. 17A-17B. Unlike columns 1710-1718 ofFIGS. 17A-17B, columns 1734-1746 may include event stream informationthat is relevant to ephemeral event streams instead of permanent eventstreams. Column 1734 may show an alphabetized or otherwise ordered orunordered list of names of groups of ephemeral event streams, and column1736 may show the number of event streams in each group. The user mayselect a user-interface element in a row of column 1750 to expand thetable to show event stream information for ephemeral event streams inthe group represented by the row. For example, the user may select theuser-interface element in the first row of column 1750 to view eventstream information for 120 ephemeral event streams belonging to thegroup named “Group_A,” as discussed in further detail below with respectto FIG. 17D.

The user may also select a value in column 1734 to view time-seriesevent data for the corresponding ephemeral event stream or group ofephemeral event streams. For example, selection of the “Group_A” valuein column 1734 may cause the GUI to navigate to a screen showing eventsand the corresponding timestamps of the ephemeral event streams of“Group_A,” graphs of metrics related to the events, and/or otherinformation associated with the events.

Column 1738 may show the names of applications used to create theephemeral event streams. Because “Enterprise Security” is selected inlist 1702, all values in column 1738 are matched to the “EnterpriseSecurity” application name, and event stream information for ephemeralevent streams associated with other applications (e.g., “Stream”) isomitted from the table.

In addition, column 1738 may allow the user to navigate from the eventstream information for a given ephemeral event stream to creationinformation for a creator of the ephemeral event stream. For example,each application name in column 1738 may include a hyperlink to a screenof the GUI for interacting with the application represented by theapplication name. The screen may show user-interface elements and/orinformation that provides context for the creation of the ephemeralevent stream. As a result, column 1738 may link the portion of the GUIused to manage the ephemeral event stream to the portion of the GUI usedto create the ephemeral event stream, which is described in furtherdetail below with respect to FIG. 17D.

Columns 1740-1744 show start times, end times, and times remaining forthe ephemeral event streams, respectively. The start times may representtimes at which generation of time-series event data for thecorresponding ephemeral event streams was initiated. For example, eachstart time may be a time at which an ephemeral event stream was createdby a capture trigger for generating additional time-series event databased on a security risk and/or an application that collects time-seriesevent data from a number of sources for subsequent analysis and/orcorrelation.

The end times may be times at which generation of time-series event datafor the corresponding ephemeral event streams is to end. For example,each end time may be a time that is a pre-specified number of minutes,hours, and/or days from the corresponding start time. The amount of timespanned between the start and end time may thus represent the durationof the ephemeral event stream, which may be selected by a capturetrigger, application, and/or user interacting with the capture triggeror application. Once the end time for an ephemeral event stream isreached, the ephemeral event stream is terminated.

The times remaining for the ephemeral event streams may indicate theamount of time left in the lifetimes of the ephemeral event streams. Forexample, each value in column 1744 may represent a “countdown” to theend time of the corresponding ephemeral event stream shown in column1742.

Column 1746 may provide a status indicating whether each ephemeral eventstream or group of ephemeral event streams is enabled or disabled. Forexample, column 1746 may indicate that the “Group_A,” “Group_B,” and“Group_E” groups of ephemeral event streams are enabled. Such enablingor disabling of ephemeral event streams may be independent of thecreation or termination of the ephemeral event streams. For example, anephemeral event stream may be created at the start time of the ephemeralevent stream by updating one or more remote capture agents withconfiguration information for the ephemeral event stream. Between thestart and end times of the ephemeral event stream, the ephemeral eventstream may be disabled to stop the generation of time-series event datafor the ephemeral event stream and/or re-enabled to resume thegeneration of time-series event data for the ephemeral event stream.Once the end time of the ephemeral event stream is reached, theephemeral event stream may be terminated, and a representation of theevent stream may be removed from the configuration information to stopthe generation of time-series event data in the event stream by theremote capture agent(s).

Like the GUIs of FIGS. 17A-17B, the GUI may provide a number ofmechanisms for managing the ephemeral event streams. First, a column1748 in the table may allow the user to perform one or more actions onindividual event streams. Each row of the table may include auser-interface element in column 1748 that, when selected, activates adrop-down menu of possible actions to be applied to the correspondingevent stream.

Within the GUI, the user-interface element in the first row of column1748 may be selected. As a result, a drop-down menu may be displayedbelow the user-interface element with a set of options, including“Disable,” “Delete,” and “Modify End Time.” The user may select“Disable” to disable generation of the ephemeral event stream fromnetwork data before the end time of the ephemeral event stream isreached. Alternatively, the “Disable” option may be replaced with an“Enable” option if the event stream (e.g., “Stream_D”) is alreadydisabled to allow the user to enable generation of the event stream fromnetwork data before the end time of the ephemeral event stream isreached.

The user may select “Delete” to delete the ephemeral event stream. If“Delete” is selected, the GUI may remove the ephemeral event stream fromthe table, even if the end time of the ephemeral event stream has notbeen reached. In turn, a representation of the event stream may beremoved from the configuration information to stop the generation oftime-series event data in the event stream by one or more remote captureagents.

The user may select “Modify End Time” to modify the end time of theephemeral event stream shown in column 1742. If “Modify End Time” isselected, the GUI may display an overlay that allows the user to specifya new end time for the ephemeral event stream as a date and time and/ora number of minutes, hours, and/or days by which the existing end timeshould be extended or reduced.

Second, user-interface element 1726 (e.g., “Bulk Edit”) may be used toapply an action associated with managing the event streams to multipleephemeral event streams. The user may use a set of checkboxes in column1708 to select the event streams to which the action is to be applied.The user may then select user-interface element 1726 to access adrop-down menu containing a set of possible actions to apply to theselected event streams. For example, the drop-down menu may includeoptions for enabling, disabling, and deleting the selected eventstreams, which are similar to options in the drop-down menu ofuser-interface elements in column 1746.

Third, user-interface element 1730 may allow the user to create a newephemeral event stream as a copy of an existing ephemeral event stream.After user-interface element 1730 is selected, an overlay may bedisplayed that includes user-interface elements for specifying a namefor the new ephemeral event stream, a description of the new eventstream, and an existing ephemeral event stream from which the newephemeral event stream is to be cloned. The new ephemeral event streammay be created with the same event attributes and/or configurationoptions as the existing ephemeral event stream, including the same endtime and/or duration as the existing ephemeral event stream. The GUI maythen show a new screen that allows the user to customize the newephemeral event stream as a variant of the existing ephemeral eventstream.

FIG. 17D shows an exemplary screenshot in accordance with the disclosedembodiments. More specifically, FIG. 17D shows the GUI of FIG. 17C afterthe user-interface element in the first row of column 1750 has beenselected. In response to the selected user-interface element, the tableincludes additional event stream information for ephemeral event streamsin the group represented by the first row in the table. As shown in FIG.17D, the additional event stream information includes an additionalgrouping of ephemeral event streams in the “Group_A” group by protocol.For example, the GUI may indicate that the 120 ephemeral event streamsin the group are further grouped into 80 ephemeral event streams forcapturing HTTP network packets, 20 ephemeral event streams for capturingFTP network packets, and 20 ephemeral event streams for capturing UDPpackets.

All ephemeral event streams in the group may be created by the sameapplication (e.g., “Enterprise Security”) and have the same start andend times. As a result, the ephemeral event streams may be created bythe application for the same purpose or similar purposes. For example,the “Enterprise Security” application may create 120 ephemeral eventstreams for generating additional time-series event data from networkpackets based on a security risk.

FIG. 17E shows an exemplary screenshot in accordance with the disclosedembodiments. In particular, FIG. 17E shows the GUI of FIG. 17C after ahyperlink (e.g., “Enterprise Security”) in the second row of column 1738has been selected. The hyperlink may navigate the user from a screen formanaging the ephemeral event stream represented by the second row of thetable to a screen containing creation information for a creator of theephemeral event stream.

The GUI of FIG. 17E may show a creator name (e.g., “Enterprise Security:Asset Investigator”) of the creator. For example, the name may specifyan application (e.g., “Enterprise Security”) and/or a feature of theapplication (e.g., “Asset Investigator”) used to create the ephemeralevent stream. The GUI may also include a portion 1758 that shows atrigger condition for creating or activating the ephemeral event stream.For example, portion 1758 may be a risk-identification mechanism thatdisplays a dashboard of time-series event data representing securityrisks. The dashboard includes a number of potential security risks, suchas “HTTP Errors,” “DNS Errors,” “Cloud Email,” “NFS Activity,” and“Threat List Activity.” Events that match one of the listed potentialsecurity risks may be represented as bars within a time intervalrepresented by the horizontal dimension of the dashboard. For example, asecurity risk 1752 may be shown as a series of bars clustered around aninterval of time under “DNS Errors” in portion 1758. The presence ofsecurity risk 1752 in portion 1758 may indicate that the triggercondition for creating the ephemeral event stream includes a potentialsecurity risk 1752, as discovered using portion 1758 in the “EnterpriseSecurity” application. To enable identification of the triggercondition, portion 1758 may replicate the timescale and data (e.g.,security risk 1752) seen by the user at the time at which the ephemeralevent stream was created using the “Enterprise Security” application.

Below portion 1758, the GUI may display additional creation information1754 describing the creator of the ephemeral event stream. For example,creation information 1754 may include a start time (e.g., “2014/09/0112:00:00”), duration (e.g., “7 days”), and/or protocol (e.g., “HTTP”)associated with network data capture by the ephemeral event stream.Creation information 1754 may describe a capture trigger for generatingadditional time-series event data based on identified security risksfrom portion 1758. For example, creation information 1754 may besubmitted through one or more user-interface elements shown belowportion 1758 in the “Enterprise Security” application to trigger thecapture of additional time-series event data in response to securityrisk 1752. After creation information 1754 is submitted to the GUI, theinformation may be used to configure the generation of the ephemeralevent stream at one or more remote capture agents.

The GUI may also include a hyperlink 1756 (e.g., “Go to StreamConfiguration”) that navigates the user back to event stream informationfor the ephemeral event stream. For example, the user may selecthyperlink 1756 to view the event stream information within the GUI ofFIG. 17C. Hyperlinks in the GUIs of FIGS. 17C-17D may thus provide amechanism for bidirectional navigation between the event streaminformation and the creation information. Such bidirectional linking mayallow the user to establish the context for creating the ephemeral eventstream as well as the current state of the ephemeral event stream, thusimproving analysis, understanding, and management of ephemeral eventstreams from multiple disparate creators.

FIG. 18 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.More specifically, FIG. 18 shows a flowchart of grouping and managingevent streams generated from captured network data. In one or moreembodiments, one or more of the steps may be omitted, repeated, and/orperformed in a different order. Accordingly, the specific arrangement ofsteps shown in FIG. 18 should not be construed as limiting the scope ofthe embodiments.

Initially, a GUI is provided on a computer system for configuring thegeneration of time-series event data from network packets captured byone or more remote capture agents (operation 1802). The GUI may includea number of user-interface elements for streamlining the creation,management, and/or update of the configuration information.

First, the GUI may provide a set of user-interface elements forspecifying a grouping of a set of event streams containing time-seriesevent data by an event stream attribute (operation 1804). For example,the GUI may enable grouping of the event streams by a category (e.g.,web, infrastructure, networking, file transfer, email, messaging,authentication, database, telephony, network management, user-createdvalue, etc.) and/or a protocol used by the network packets (e.g.,transport layer protocol, session layer protocol, presentation layerprotocol, application layer protocol). The GUI may also enable groupingof the event streams by applications used to create the event streams(e.g., based on application name) and/or event stream lifecycles of theevent stream (e.g., permanent or ephemeral).

Next, the GUI may display a set of user-interface elements containingevent stream information for one or more subsets of the event streamsrepresented by the grouping of the event streams by the event streamattribute (operation 1806). Grouping of displayed event streaminformation by event stream attributes is described in further detailbelow with respect to FIG. 19.

Finally, the GUI may provide a set of user-interface elements formanaging the event streams (operation 1808). For example, the GUI may beused to clone a new event stream from an existing event stream, createan event stream, delete an event stream, enable an event stream, disablean event stream, and/or modify an end time of an ephemeral event stream,as discussed above with respect to FIGS. 17A-17E.

FIG. 19 shows a flowchart illustrating the process of displaying eventstream information represented by a grouping of the event streams by anevent stream attribute in accordance with the disclosed embodiments. Inone or more embodiments, one or more of the steps may be omitted,repeated, and/or performed in a different order. Accordingly, thespecific arrangement of steps shown in FIG. 19 should not be construedas limiting the scope of the embodiments.

First, one or more values of an event stream attribute are displayed(operation 1902), and displayed event stream information is grouped intoone or more subsets of the event streams based on the value(s) of theevent stream attribute (operation 1904). For example, a user may specifythe type of event stream attribute to group by, and a GUI may displayone or more categories, protocols, application names, and/or othervalues of the event stream attribute in a list. After a given value ofthe event stream attribute is selected from the list, the GUI may showevent stream information matching the selected value in a table next tothe list.

The event stream information may be grouped by an additional eventstream attribute (operation 1906). If the event stream information isnot to be grouped by an additional event stream attribute, grouping ofthe displayed event stream information by the first event streamattribute is maintained.

If the event stream information is to be grouped by an additional eventstream attribute, one or more values of the additional event streamattribute are displayed (operation 1902), and the displayed event streaminformation is further grouped into one or more additional subsets ofthe event streams based on the value(s) of the additional event streamattribute (operation 1904). Continuing with the above example, the eventstream information in the table may additionally be grouped and/orfiltered by an event stream lifecycle of the event streams, which may bepermanent or ephemeral. If a permanent event stream lifecycle isselected (e.g., through the GUI), event stream information for permanentevent streams that match the value of the first event stream attribute(e.g., category, protocol, application) is shown. Such event streaminformation may include a name, a type, a protocol, an application, adescription, a status, and/or a graph of a metric associated with thetime-series event data of the event streams. If an ephemeral eventstream lifecycle is selected, event stream information for ephemeralevent streams that match the value of the first event stream attributeis shown. Such event stream information may include a name, a number ofevent streams, an application, a start time, an end time, a timeremaining, and/or a status.

The displayed event stream information may continue to be grouped byadditional event stream attributes (operation 1906) to furtherfacilitate the creation, search, and/or management of event streamsacross multiple applications, categories, protocols, and/or other eventstream attributes. For example, the displayed event stream informationmay be grouped by category, protocol, keyword, and/or event streamlifecycle to allow a user to find event streams associated with a givencategory, protocol, keyword, and/or event stream lifecycle. For eachevent stream attribute by which the event stream information is to begrouped, one or more values of the event stream attribute are displayed(operation 1902). The displayed event stream information, which mayalready be grouped by one or more other event stream attributes, is thenfurther grouped or filtered into one or more subsets of the eventstreams based on the value(s) of the event stream attribute (operation1904). Grouping of displayed event stream information by values of eventstream attributes may continue until the displayed event streaminformation has been grouped by values for all relevant event streamattributes.

FIG. 20 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.More specifically, FIG. 20 shows a flowchart of providing inlinevisualizations of metrics related to captured network data. In one ormore embodiments, one or more of the steps may be omitted, repeated,and/or performed in a different order. Accordingly, the specificarrangement of steps shown in FIG. 20 should not be construed aslimiting the scope of the embodiments.

Initially, a set of event streams is obtained from one or more remotecapture agents over one or more networks (operation 2002). The eventstreams may include time-series event data generated from networkpackets captured by the remote capture agent(s). Next, event streaminformation for each event stream and a graph of a metric associatedwith the time-series event data in the event stream are displayed withina GUI on a computer system (operation 2004). The graph may include asparkline, bar graph, line chart, histogram, and/or other type ofvisualization of the metric that is shown in line with the event streaminformation. The metric may include network traffic, a number of events,and/or a number of notable events (e.g., security risks).

A subset of event streams associated with a grouping of the eventstreams by an event stream attribute is also obtained (operation 2006).For example, the subset of event streams may match a value of one ormore event stream attributes. Alternatively, the subset of event streamsmay include all event streams if the event streams are matched to allpossible values of the event stream attribute(s). Next, the metric isaggregated across the subset of the event streams (operation 2008), anda graph of the aggregated metric across the event streams is displayedwithin the GUI (operation 2010). For example, the metric may beaggregated as a sum, average, and/or other summary statistic, and thegraph of the aggregated metric may include a sparkline and/or othervisual representation of the aggregated metric over time and/or anotherdimension.

While the graphs for individual event streams and the aggregated metricacross the event streams are displayed, the graphs are updated inreal-time with time-series event data from the remote capture agent(s)(operation 2012). For example, sparklines representing individual andaggregate network traffic over time may “advance” to reflect newlyreceived time-series event data from the remote capture agent(s). Thegraph(s) are also updated with the value of the metrics or aggregatedmetric based on the position of a cursor over the graph(s) (operation2014). For example, the numeric value of a metric (e.g., networktraffic, number of events, number of notable events, etc.) at a givenpoint in time may be displayed in response to the positioning of acursor over that point in time in the graph.

Finally, event stream information for the subset of the event streams isdisplayed (operation 2016). For example, the event stream informationmay be displayed in a table, and the graphs for individual event streamsmay be shown in a column of the table. The graph of the aggregatedmetric may be displayed in a different part of the GUI, and the graphsmay be updated based on the event streams and/or groupings shown in thetable.

FIG. 21 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.More specifically, FIG. 21 shows a flowchart of managing ephemeral eventstreams generated from captured network data. In one or moreembodiments, one or more of the steps may be omitted, repeated, and/orperformed in a different order. Accordingly, the specific arrangement ofsteps shown in FIG. 21 should not be construed as limiting the scope ofthe embodiments.

First, a GUI is provided on a computer system for obtainingconfiguration information for configuring the generation of time-seriesevent data from network packets captured by one or more remote captureagents (operation 2102). Next, a subset of one or more ephemeral eventstreams associated with a grouping of the ephemeral event stream(s) byan event stream attribute is obtained (operation 2104). For example, thesubset of ephemeral event stream(s) may be associated with a grouping ofthe ephemeral event streams by one or more categories, applicationsand/or protocols. The ephemeral event streams may be used to temporarilygenerate time-series event data from network packets captured by theremote capture agent(s).

The GUI is used to display event stream information for the ephemeralevent stream(s) (operation 2106), along with a set of user-interfaceelements for managing the ephemeral event stream(s) (operation 2108).The user-interface elements may be used to disable an ephemeral eventstream, delete an ephemeral event stream, and/or modify an end time forterminating the ephemeral event stream. The event stream information mayinclude a name, number of event streams, application, start time, endtime, time remaining, and/or status.

The GUI also includes a set of user-interface elements for creating anephemeral event stream (operation 2110), as well as a mechanism forapplying an action associated with managing the ephemeral eventstream(s) to a set of selected ephemeral event streams (operation 2112).For example, the GUI may enable the creation of an ephemeral eventstream as a copy (e.g., clone) of an existing ephemeral event stream.The GUI may also allow multiple ephemeral event streams to be enabled,disabled, and/or deleted.

The configuration information is updated based on input received throughthe GUI (operation 2114) and provided over the network to the remotecapture agent(s) (operation 2116). The configuration information maythen be used to configure the generation of the time-series event dataat the remote capture agent(s) during runtime of the remote captureagent(s). For example, the configuration information may be used tocreate, delete, enable, disable, and/or modify the end times of one ormore ephemeral event streams.

FIG. 22 shows a flowchart illustrating the process of facilitating theprocessing of network data in accordance with the disclosed embodiments.More specifically, FIG. 22 shows a flowchart of bidirectional linking ofephemeral event streams to creators of the ephemeral event streams. Inone or more embodiments, one or more of the steps may be omitted,repeated, and/or performed in a different order. Accordingly, thespecific arrangement of steps shown in FIG. 22 should not be construedas limiting the scope of the embodiments.

First, a GUI is provided on a computer system for obtainingconfiguration information for configuring the generation of time-seriesevent data from network packets captured by one or more remote captureagents (operation 2202). Next, a subset of one or more ephemeral eventstreams associated with a grouping of the ephemeral event stream(s) byan event stream attribute is obtained (operation 2204), and event streaminformation for the ephemeral event stream(s) is displayed in a GUI(operation 2206), as described above.

The GUI is used to provide a mechanism for navigating between the eventstream information and creation information for one or more creators ofthe ephemeral event stream(s) (operation 2208). The mechanism mayinclude a hyperlink from the event stream information to the creationinformation for a creator of an ephemeral event stream and/or ahyperlink from the creation information back to the event streaminformation. The creation information may include a creator name, aprotocol, a duration of the ephemeral event stream, and/or a triggercondition for activating the ephemeral event stream. Creators ofephemeral event streams may include applications for monitoring networktraffic captured by the remote capture agent(s) and/or capture triggersfor generating additional time-series event data from network packets onthe remote capture agent(s) based on a security risk.

The GUI also includes a set of user-interface elements containing thetime-series event data (operation 2210). For example, the GUI may showindividual events and the associated timestamps, graphs of metricsassociated with the events, and/or other representations of events inthe ephemeral event stream(s). Consequently, the GUI may facilitateunderstanding and analysis of both the content and context of theephemeral event streams.

Custom Content Extraction

FIG. 23A presents a block diagram illustrating a system that facilitatescustom content extraction from network packets in accordance with thedisclosed embodiments. During operation, the system receives a packetstream 2302 to be processed. For example, this packet stream can bereceived at a remote capture agent situated at a network interface at aspecific location within a network. Next, packet stream 2302 feeds intoa multi-protocol packet parser 2304 that determines a protocol for thepacket based on the structure of the packet. For example, in someembodiments, the multi-protocol packet parser 2304 comprises adeep-packet inspection (DPI) engine.

After the protocol for each packet is determined, the system feedspackets associated with one or more target protocols into a custom fieldparser 2308. Custom field parser 2308 applies one or morecustom-content-extraction rules to each packet associated with a targetprotocol to obtain the extracted content 2310. In some embodiments,applying the custom-content-extraction rule to a given packet involvesapplying a field-specific regular expression to one or more targetfields in the packet. In other embodiments, applying thecustom-content-extraction rule to a given packet involves applying afield-specific extraction rule for a structured data format, which caninclude XML or JSON, to one or more target fields in the packet.Finally, the extracted content 2310 is formed into a set of events thatare stored in data store 103 to facilitate queries involving theextracted content 2310. More specifically, as illustrated in FIG. 23B,the system obtains extracted content 2324 from a field 2322 in a packet2320. Next, the system incorporates this extracted content 2324 into anevent 2326, wherein event 2326 is subsequently stored in data store 103.Note that extracted content 2324 can comprise a portion of an event, anentire event, or can be split across multiple events.

At a later time, after extracted content 2310 is stored in data store103, a user 2309 submits a query 2311 to a query processor 2314. Next,query processor 2314 applies the query to the extracted content 2310retrieved from data store 103 to produce query results 2316. In someembodiments, the system uses a late-binding schema generated from query2311 to retrieve data values from the events in the data store.

Processing Packets at Data-Ingestion Time

FIG. 24 presents a flowchart illustrating how the packets are processedto facilitate custom content extraction in accordance with the disclosedembodiments. During this process, the system receives a stream ofpackets (step 2402). The system then parses packets in the receivedstream to determine a protocol for each packet (step 2404). Next, thesystem applies a custom-content-extraction rule to each packetassociated with a target protocol specified by thecustom-content-extraction rule to obtain the extracted content (step2406). Finally, the system stores the extracted content in events in adata store to facilitate subsequent queries involving the extractedcontent (step 2408).

Processing a Query

FIG. 25 presents a flowchart illustrating how a query is processed inaccordance with the disclosed embodiments. First, the system receives aquery to be applied to the events in the data store (step 2502). Inresponse to this query, the system retrieves events from the data store(step 2504), and uses a late-binding schema generated from the query toretrieve data values from the events (step 2506). Finally, the systemprocesses the query using the retrieved data values (step 2508).

Extraction Rule Dialog

FIG. 26 illustrates a dialog box 2602 that is configured to receive acustom-content-extraction rule from a user in accordance with thedisclosed embodiments. Dialog box 2602 includes a number of fields forreceiving input from a user, including: (1) a source field 2604configured to receive an identifier for a source field for the extractedcontent in each packet; (2) an extraction type field 2606 configured toreceive a specifier for a type of extraction rule to be used to obtainthe extracted content (e.g., a regular expression or a rule specified instructured data format, such as XML or JSON); (3) an extraction rulefield 2608 configured to receive an extraction rule entered by the user;(4) a new field 2610 configured to receive a new identifier that is usedto identify the extracted content; and (5) a description field 2612configured to receive a textual description of how the data isextracted.

Computer System

FIG. 27 shows a computer system 2700. Computer system 2700 includes aprocessor 2702, memory 2704, storage 2706, and/or other components foundin electronic computing devices. Processor 2702 may support parallelprocessing and/or multi-threaded operation with other processors incomputer system 2700. Computer system 2700 may also include input/output(I/O) devices such as a keyboard 2708, a mouse 2710, and a display 2712.

Computer system 2700 may include functionality to execute variouscomponents of the present embodiments. In particular, computer system2700 may include an operating system (not shown) that coordinates theuse of hardware and software resources on computer system 2700, as wellas one or more applications that perform specialized tasks for the user.To perform tasks for the user, applications may obtain the use ofhardware resources on computer system 2700 from the operating system, aswell as interact with the user through a hardware and/or softwareframework provided by the operating system.

In one or more embodiments, computer system 2700 provides a system forfacilitating the processing of network data. The system may include aconfiguration server. The configuration server may provide a GUI forobtaining configuration information for configuring the generation oftime-series event data from network packets captured by a remote captureagent. The GUI may include a set of user-interface elements forspecifying a grouping of a set of event streams containing thetime-series event data by an event stream attribute associated with theevent streams. The GUI may also include a set of user-interface elementscontaining event stream information for one or more subsets of the eventstreams represented by the grouping of the event streams by the eventattribute. The GUI may further include graphs of metrics associated withthe time-series event data in the event streams and a graph of anaggregated metric across the set of event streams.

The GUI may additionally include a set of user-interface elements forcreating and managing the event streams, including one or more ephemeralevent streams for temporarily generating time-series event data fromnetwork packets. Finally, the GUI may include a mechanism for navigatingbetween event stream information for the ephemeral event streams andsource information for one or more sources of the ephemeral eventstreams. Input received through the GUI may be used to updateconfiguration information, which is provided over a network to theremote capture agent and used to configure the generation of time-seriesevent data at the remote capture agent during runtime of the remotecapture agent.

In addition, one or more components of computer system 2700 may beremotely located and connected to the other components over a network.Portions of the present embodiments (e.g., remote capture agent,configuration server, GUI, etc.) may also be located on different nodesof a distributed system that implements the embodiments. For example,the present embodiments may be implemented using a cloud computingsystem that manages the creation, update, and deletion of event streamsat a set of distributed remote capture agents.

The foregoing descriptions of various embodiments have been presentedonly for purposes of illustration and description. They are not intendedto be exhaustive or to limit the present invention to the formsdisclosed. Accordingly, many modifications and variations will beapparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present invention.

1.-30. (canceled)
 31. A computer-implemented method, comprising:receiving, via a graphical user interface (GUI), input defining a customcontent extraction rule, the input including: first input specifying asource field in network packets to be monitored by a remote captureagent, the source field containing structured data, and second inputspecifying an extraction rule to be used to extract data from thestructured data to obtain extracted data; generating configurationinformation based on the input; and sending the configurationinformation to the remote capture agent, the configuration informationcausing the remote capture agent to generate timestamped events, thetimestamped events including extracted data obtained by applying thecustom content extraction rule to network packets monitored by theremote capture agent.
 32. The computer-implemented method of claim 31,wherein the remote capture agent generates the timestamped events by,for each network packet of a plurality of network packets monitored bythe remote capture agent: parsing the network packet to identify astructure of the network packet, the structure of the network packetused to determine a protocol associated with the network packet;applying the extraction rule associated with the protocol to the networkpacket to obtain extracted content, wherein applying the extraction ruleincludes: identifying at least one user-specified field in the networkpacket containing structured data from which the extracted content is tobe obtained, and extracting data from the structured data contained inthe user-specified field of the network packet; generating a timestampedevent including a field storing the extracted content; and sending thetimestamped event including the extracted content to another componenton a computer network for storage in a data store, the data storefacilitating the querying of timestamped event data stored in the datastore using late-binding schemas generated from received queries. 33.The computer-implemented method of claim 31, wherein the method furthercomprises: storing the timestamped events in a data store; receiving aquery to be applied to the timestamped events stored in the data store;retrieving timestamped events from the data store satisfying the query;using a late-binding schema generated from the query to retrieve datavalues from the retrieved timestamped events; and processing the queryusing the retrieved data values.
 34. The computer-implemented method ofclaim 31, wherein the input further specifies a protocol to beassociated with the custom content extraction rule.
 35. Thecomputer-implemented method of claim 31, wherein the input furtherincludes third input specifying a field-specific regular expression tobe applied to the source field in the network packets, and whereinapplying the custom content extraction rule to the network packetsincludes applying the field-specific regular expression to the sourcefield in the network packets.
 36. The computer-implemented method ofclaim 31, wherein the structured data includes eXtensible MarkupLanguage (XML) formatted data, and wherein applying the custom contentextraction rule includes extracting data from the XML-formatted data.37. The computer-implemented method of claim 31, wherein the structureddata includes JavaScript Object Notation (JSON) formatted data, andwherein applying the custom content extraction rule includes extractingdata from the JSON-formatted data.
 38. The computer-implemented methodof claim 31, wherein the custom content extraction rule is associatedwith a protocol, and wherein the remote capture agent uses a deep-packetinspection engine to determine a protocol associated with the networkpackets.
 39. The computer-implemented method of claim 31, wherein theinput further includes third input specifying an extraction rule typethat identifies a type of extraction rule to be used to obtain theextracted data.
 40. The computer-implemented method of claim 31, whereinthe configuration information causes the remote capture agent to sendthe timestamped events to another component for storage in a data store.41. A non-transitory computer-readable storage medium storinginstructions which, when executed by one or more processors, causeperformance of operations comprising: receiving, via a graphical userinterface (GUI), input defining a custom content extraction rule, theinput including: first input specifying a source field in networkpackets to be monitored by a remote capture agent, the source fieldcontaining structured data, and second input specifying an extractionrule to be used to extract data from the structured data to obtainextracted data; generating configuration information based on the input;and sending the configuration information to the remote capture agent,the configuration information causing the remote capture agent togenerate timestamped events, the timestamped events including extracteddata obtained by applying the custom content extraction rule to networkpackets monitored by the remote capture agent.
 42. The non-transitorycomputer-readable storage medium of claim 41, wherein the remote captureagent generates the timestamped events by, for each network packet of aplurality of network packets monitored by the remote capture agent:parsing the network packet to identify a structure of the networkpacket, the structure of the network packet used to determine a protocolassociated with the network packet; applying the extraction ruleassociated with the protocol to the network packet to obtain extractedcontent, wherein applying the extraction rule includes: identifying atleast one user-specified field in the network packet containingstructured data from which the extracted content is to be obtained, andextracting data from the structured data contained in the user-specifiedfield of the network packet; generating a timestamped event including afield storing the extracted content; and sending the timestamped eventincluding the extracted content to another component on the computernetwork for storage in a data store, the data store facilitating thequerying of timestamped event data stored in the data store usinglate-binding schemas generated from received queries.
 43. Thenon-transitory computer-readable storage medium of claim 41, wherein theinstructions, when executed by the one or more processors, further causeperformance of operations comprising: storing the timestamped events ina data store; receiving a query to be applied to the timestamped eventsstored in the data store; retrieving timestamped events from the datastore satisfying the query; using a late-binding schema generated fromthe query to retrieve data values from the retrieved timestamped events;and processing the query using the retrieved data values.
 44. Thenon-transitory computer-readable storage medium of claim 41, wherein theinput further specifies a protocol to be associated with the customcontent extraction rule.
 45. The non-transitory computer-readablestorage medium of claim 41, wherein the input further includes thirdinput specifying a field-specific regular expression to be applied tothe source field in the network packets, and wherein applying the customcontent extraction rule to the network packets includes applying thefield-specific regular expression to the source field in the networkpackets.
 46. The non-transitory computer-readable storage medium ofclaim 41, wherein the structured data includes eXtensible MarkupLanguage (XML) formatted data, and wherein applying the custom contentextraction rule includes extracting data from the XML-formatted data.47. The non-transitory computer-readable storage medium of claim 41,wherein the structured data includes JavaScript Object Notation (JSON)formatted data, and wherein applying the custom content extraction ruleincludes extracting data from the JSON-formatted data.
 48. Thenon-transitory computer-readable storage medium of claim 41, wherein thecustom content extraction rule is associated with a protocol, andwherein the remote capture agent uses a deep-packet inspection engine todetermine a protocol associated with the network packets.
 49. Thenon-transitory computer-readable storage medium of claim 41, wherein theinput further includes third input specifying an extraction rule typethat identifies a type of extraction rule to be used to obtain theextracted data.
 50. The non-transitory computer-readable storage mediumof claim 41, wherein the configuration information causes the remotecapture agent to send the timestamped events to another component forstorage in a data store.
 51. An apparatus, comprising: one or moreprocessors; a non-transitory computer-readable storage medium storinginstructions which, when executed by the one or more processors, causesthe apparatus to: receive, via a graphical user interface (GUI), inputdefining a custom content extraction rule, the input including: firstinput specifying a source field in network packets to be monitored by aremote capture agent, the source field containing structured data, andsecond input specifying an extraction rule to be used to extract datafrom the structured data to obtain extracted data; generateconfiguration information based on the input; and send the configurationinformation to the remote capture agent, the configuration informationcausing the remote capture agent to generate timestamped events, thetimestamped events including extracted data obtained by applying thecustom content extraction rule to network packets monitored by theremote capture agent.
 52. The apparatus of claim 51, wherein the remotecapture agent generates the timestamped events by, for each networkpacket of a plurality of network packets monitored by the remote captureagent: parsing the network packet to identify a structure of the networkpacket, the structure of the network packet used to determine a protocolassociated with the network packet; applying the extraction ruleassociated with the protocol to the network packet to obtain extractedcontent, wherein applying the extraction rule includes: identifying atleast one user-specified field in the network packet containingstructured data from which the extracted content is to be obtained, andextracting data from the structured data contained in the user-specifiedfield of the network packet; generating a timestamped event including afield storing the extracted content; and sending the timestamped eventincluding the extracted content to another component on a computernetwork for storage in a data store, the data store facilitating thequerying of timestamped event data stored in the data store usinglate-binding schemas generated from received queries.
 53. The apparatusof claim 51, wherein the instructions, when executed by the one or moreprocessors, further cause the apparatus to: store the timestamped eventsin a data store; receive a query to be applied to the timestamped eventsstored in the data store; retrieve timestamped events from the datastore satisfying the query; use a late-binding schema generated from thequery to retrieve data values from the retrieved timestamped events; andprocess the query using the retrieved data values.
 54. The apparatus ofclaim 51, wherein the input further specifies a protocol to beassociated with the custom content extraction rule.
 55. The apparatus ofclaim 51, wherein the input further includes third input specifying afield-specific regular expression to be applied to the source field inthe network packets, and wherein applying the custom content extractionrule to the network packets includes applying the field-specific regularexpression to the source field in the network packets.
 56. The apparatusof claim 51, wherein the structured data includes eXtensible MarkupLanguage (XML) formatted data, and wherein applying the custom contentextraction rule includes extracting data from the XML-formatted data.57. The apparatus of claim 51, wherein the structured data includesJavaScript Object Notation (JSON) formatted data, and wherein applyingthe custom content extraction rule includes extracting data from theJSON-formatted data.
 58. The apparatus of claim 51, wherein the customcontent extraction rule is associated with a protocol, and wherein theremote capture agent uses a deep-packet inspection engine to determine aprotocol associated with the network packets.
 59. The apparatus of claim51, wherein the input further includes third input specifying anextraction rule type that identifies a type of extraction rule to beused to obtain the extracted data.
 60. The apparatus of claim 51,wherein the configuration information causes the remote capture agent tosend the timestamped events to another component for storage in a datastore.